Experiment 4: Document at various levels for covid scice large and covid psychology large datasets

1. Article level

#importing libraries
import os.path
from gensim import corpora
from gensim.models import LsiModel
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from gensim.models.coherencemodel import CoherenceModel
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import sklearn as s
import dataframe_image as dfi
import networkx as nx
import mantel

C:\ProgramData\Anaconda3\lib\site-packages\gensim\similarities\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.
  warnings.warn(msg)

LSA on covid-science.txt at article level

def load_data(path,file_name,seperator):
    """"
    Input  : path and file_name
    function: loading text file
    Output : number of words, document list
    """
    
    documents_list = []
    titles=[]
    document = ''
    i=0
    with open( os.path.join(path, file_name) ,"r", encoding="utf8") as fin:
        data = fin.read()
        words = data.split()
        fin.seek(0)
        print('Number of words in text file :', len(words))
        for line in fin.readlines():
            text = line.strip()
            i=i+1
            if text !='':
                document = document+text
                if text == seperator:
                    documents_list.append(document)
                    document = ''
        documents_list.append(document)
    print("Total Number of Documents:",len(documents_list))
    titles.append( text[0:min(len(text),100)] )
    return documents_list,titles,len(words)

document_list,titles,word_count_science=load_data("","covid-science-large.txt",'NEW_PAPER')

Number of words in text file : 274941
Total Number of Documents: 40

def preprocess_data(doc_set):
    """
    Input  : list of documents
    function: text preprocessing - word tokenize, removing english stopwords, and potter stemming 
    Output : clean text
    """
    # initialize regex tokenizer
    word_tokenizer = RegexpTokenizer(r'\w+')
    # create English stop words list
    eng_stop = set(stopwords.words('english'))
    # Create p_stemmer of class PorterStemmer
    po_stemmer = PorterStemmer()
    # list for tokenized documents in loop
    texts = []
    # loop through document list
    for i in doc_set:
        # clean and tokenize document string
        raw = i.lower()
        tokens = word_tokenizer.tokenize(raw)
        tokens = [i for i in tokens if len(i)>3]
        print(tokens)
        # removing english stop words from tokens
        stopped_tokens = [i for i in tokens if not i in eng_stop]
        # stemmed tokens
        stem_tokens = [po_stemmer.stem(i) for i in stopped_tokens]
        # list of tokens
        texts.append(stem_tokens)
    return texts

clean_text=preprocess_data(document_list)

['covidgr', 'dataset', 'covid', 'sdnetmethodology', 'predicting', 'covid', '19based', 'chest', 'imagess', 'tabik', 'gómez', 'ríos', 'martín', 'rodríguez', 'sevillano', 'garcía', 'area', 'charte', 'guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'herreraabstract', 'currently', 'coronavirus', 'disease', 'covid', 'most', 'infectious', 'diseases', '21st', 'century', 'diagnosed', 'using', 'testing', 'scans', 'orchest', 'images', 'computed', 'tomography', 'scanners', 'testing', 'available', 'mostmedical', 'centers', 'hence', 'many', 'cases', 'imagesbecome', 'most', 'time', 'cost', 'effective', 'tool', 'assisting', 'clinicians', 'making', 'decisions', 'deep', 'learning', 'neural', 'networkshave', 'great', 'potential', 'building', 'covid', 'triage', 'systemsand', 'detecting', 'covid', 'patients', 'especially', 'patients', 'withlow', 'severity', 'unfortunately', 'current', 'databases', 'allowbuilding', 'such', 'systems', 'they', 'highly', 'heterogeneousand', 'biased', 'towards', 'severe', 'cases', 'this', 'article', 'threefold', 'demystify', 'high', 'sensitivities', 'achieved', 'bymost', 'recent', 'covid', 'classification', 'models', 'under', 'aclose', 'collaboration', 'with', 'hospital', 'universitario', 'clínico', 'sancecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'that', 'includes', 'levelsmanuscript', 'received', 'september', '2020', 'revised', 'october', '2020', 'accepted', 'november', '2020', 'date', 'publication', 'november', '2020', 'date', 'current', 'version', 'december', '2020', 'this', 'work', 'supported', 'bythe', 'project', 'deepscop', 'ayudas', 'fundación', 'bbva', 'equipos', 'investigación', 'científica', 'data', '2018', 'covid19_rx', 'ayudas', 'fundaciónbbva', 'equipos', 'investigación', 'científica', 'sars', 'covid', '192020', 'spanish', 'ministry', 'science', 'technology', 'under', 'theproject', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 'cajalprogramme', '2015', '18136', 'gómez', 'ríos', 'supported', 'thefpu', 'programme', 'fpu16', '04765', 'charte', 'supported', 'fpuprogramme', 'fpu17', '04069', 'suárez', 'supported', 'programme', 'fpu18', '05989', 'supported', 'european', 'researchcouncil', 'grant', 'agreement', '647038', 'biodesert', 'this', 'projectis', 'approved', 'provincial', 'research', 'ethics', 'committee', 'granada', 'corresponding', 'author', 'siham', 'tabik', 'tabik', 'gómez', 'ríos', 'sevillano', 'garcía', 'charte', 'suárez', 'luengo', 'herrera', 'with', 'andalusian', 'research', 'institute', 'indata', 'science', 'computational', 'intelligence', 'university', 'granada', '18071', 'granada', 'spain', 'mail', 'siham', 'anabelgrios', 'decsai', 'isega24ivan', 'gmail', 'fdavidcl', 'jlsuarezdiaz', 'julianlm', 'decsai', 'herrera', 'decsai', 'martín', 'rodríguez', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'with', 'hospital', 'universitario', 'clínicosan', 'cecilio', 'granada', '36310', 'spain', 'mail', 'joseluismartin', 'hotmail', 'valerogonzalez', 'yahoo', 'pgvillanova', 'gmail', 'euolm', 'yahoo', 'area', 'with', 'atlanttic', 'research', 'center', 'telecommunication', 'technologies', 'university', 'vigo', 'galicia', 'spain', 'mail', 'mreyarea', 'gmail', 'guirado', 'with', 'multidisciplinary', 'institute', 'environmentstudies', 'ramón', 'margalef', 'university', 'alicante', '03690', 'spain', 'mail', 'geesecillo', 'gmail', 'digital', 'object', 'identifier', '1109', 'jbhi', '2020', '3037127of', 'severity', 'from', 'normal', 'with', 'positive', 'mild', 'moderate', 'severe', 'covidgr', 'contains', 'positive', 'and426', 'negative', 'posteroanterior', 'views', 'wepropose', 'covid', 'smart', 'data', 'based', 'network', 'covid', 'sdnet', 'methodology', 'improving', 'generalization', 'capacity', 'ofcovid', 'classification', 'models', 'approach', 'reaches', 'goodand', 'stable', 'results', 'with', 'accuracy', 'severe', 'moderate', 'andmild', 'covid', 'severity', 'levels', 'approach', 'could', 'help', 'inthe', 'early', 'detection', 'covid', 'covidgr', 'along', 'withthe', 'severity', 'level', 'labels', 'available', 'scientific', 'community', 'through', 'this', 'link', 'https', 'dasci', 'transferencia', 'open', 'data', 'covidgr', 'index', 'terms', 'covid', 'convolutional', 'neural', 'networks', 'smart', 'data', 'introductionin', 'last', 'months', 'world', 'been', 'witnessing', 'howcovid', 'pandemic', 'increasingly', 'infecting', 'large', 'massof', 'people', 'very', 'fast', 'everywhere', 'world', 'trends', 'arenot', 'clear', 'some', 'research', 'confirm', 'that', 'this', 'problem', 'maypersist', 'until', '2024', 'besides', 'prevalence', 'studies', 'conducted', 'inseveral', 'countries', 'reveal', 'that', 'tiny', 'proportion', 'populationhave', 'developed', 'antibodies', 'after', 'exposure', 'virus', 'spain', 'this', 'means', 'that', 'frequently', 'large', 'number', 'patientswill', 'need', 'assessed', 'small', 'time', 'intervals', 'numberof', 'clinicians', 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'less', 'cumbersome', 'lightweightand', 'transportable', 'general', 'this', 'type', 'resources', 'moreavailable', 'than', 'required', 'scan', 'tests', 'addition', 'test', 'takes', 'about', 'seconds', 'patient', 'which', 'makes', 'most', 'time', 'cost', 'effectiveassessment', 'tools', 'recent', 'studies', 'provide', 'estimates', 'expert', 'radiologistssensitivity', 'diagnosis', 'covid', 'based', 'scans', 'study', 'patients', 'with', 'chestct', 'essay', 'performed', 'within', 'days', 'reported', 'asensitivity', 'compared', 'with', 'sensitivityof', 'different', 'study', 'patients', 'mean', 'age56', 'years', 'reported', 'sensitivity', 'comparedwith', 'initial', 'according', 'analysis', '636ambulatory', 'patients', 'most', 'patients', 'presenting', 'urgent', 'carecenters', 'with', 'confirmed', 'coronavirus', 'disease', '2019', 'have', 'normal', 'ormildly', 'abnormal', 'findings', 'only', 'these', 'patientsare', 'correctly', 'diagnosed', 'expert', 'recent', 'study', 'authors', 'proposed', 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'scarcity', 'from', 'covid', 'cases', 'although', 'deeplearning', 'based', 'recognition', 'techniques', 'dominant', 'puter', 'vision', 'where', 'they', 'achieved', 'state', 'performance', 'their', 'performance', 'degrades', 'fast', 'data', 'scarcity', 'which', 'isthe', 'reality', 'this', 'problem', 'hand', 'this', 'study', 'aims', 'addresssuch', 'limitations', 'proposing', 'robust', 'highly', 'accuratecovid', 'recognition', 'approach', 'directly', 'from', 'images', 'proposed', 'approach', 'based', 'csen', 'that', 'seenas', 'bridge', 'between', 'deep', 'learning', 'models', 'representation', 'based', 'methods', 'csen', 'uses', 'both', 'dictionary', 'oftraining', 'samples', 'learn', 'direct', 'mapping', 'from', 'querysamples', 'sparse', 'support', 'representation', 'coefficients', 'with', 'this', 'unique', 'ability', 'having', 'advantage', 'compactnetwork', 'proposed', 'csen', 'based', 'covid', 'recognitionsystems', 'surpass', 'competing', 'methods', 'achieve', 'over', 'sensitivity', 'over', 'specificity', 'furthermore', 'they', 'yieldthe', 'most', 'computationally', 'efficient', 'scheme', 'terms', 'speedand', 'memory', 'acknowledgmentthe', 'authors', 'would', 'like', 'thank', 'following', 'medicaldoctor', 'team', 'their', 'generous', 'feedbacks', 'continuousyamaç', 'convolutional', 'sparse', 'support', 'estimator', 'based', 'covid', 'recognition', '1819proof', 'reading', 'khalid', 'hameed', 'reem', 'medicalcenter', 'doha', 'qatar', 'tahir', 'hamid', 'consultant', 'cardiologist', 'inhamad', 'medical', 'corporation', 'hospital', 'with', 'weill', 'cornellmedicine', 'qatar', 'doha', 'rashid', 'mazhar', 'hamadmedical', 'corporation', 'hospital', 'doha', 'qatar']

# preparing corpus for LSA
def prepare_corpus(doc_clean):
    """
    Input  : preprossed document
    function: creating term dictionary and document term matrix
    Output : term dictionary and Document Term Matrix
    """
    # every unique term is assigned an index. dictionary = corpora.Dictionary(doc_clean)
    term_dictionary = corpora.Dictionary(doc_clean)
    # Converting list of documents (corpus) into Document Term Matrix using dictionary prepared above.
    docu_term_matrix = [term_dictionary.doc2bow(doc) for doc in doc_clean]
    # generate LDA model
    return term_dictionary,docu_term_matrix

def create_gensim_lsa_model(doc_clean,number_of_topics,words):
    """
    Input  : preprocessed document, number of topics and word count
    function: create LSA model using gensim
    Output : return LSA model
    """
    term_dictionary,docu_term_matrix=prepare_corpus(doc_clean)
    # LSA model
    lsamodel = LsiModel(docu_term_matrix, num_topics=number_of_topics, id2word = term_dictionary)  # train model
    return lsamodel

# LSA Model
number_of_topics=25
words=word_count_science
model_science=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_science =dict(model_science.show_topic(0, topn=words))##dict and encoding matrix values

#These variables are for further experimentation in vertical comparission mantel test
model_science_articles = create_gensim_lsa_model(clean_text,number_of_topics,words)

#top 5 words from model lsa
dict(model_science.show_topic(0, topn=5))

{'covid': 0.5140048530952065,
 'imag': 0.25326452105798253,
 'model': 0.23714000415930525,
 'use': 0.23493416886438223,
 'data': 0.21392165729076498}

#wordcloud of top 10 words from the documents
from wordcloud import WordCloud
text = dict(model_science.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph21.png')

# cosine similarity of top 5 words

a=pd.DataFrame()

for i in range(number_of_topics):
    words_science =dict(model_science.show_topic(i, topn=words))
    b=pd.DataFrame(words_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_science.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled24.png')

cosine-similarity
           covid       imag      model        use       data
covid          1   0.107455  0.0469536   0.263832   0.172124
imag    0.107455          1 -0.0734307   0.108349 -0.0538667
model  0.0469536 -0.0734307          1  0.0857265  0.0055496
use     0.263832   0.108349  0.0857265          1  0.0192471
data    0.172124 -0.0538667  0.0055496  0.0192471          1

#networkx for top 5 words
df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='green',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig24.png')

LSA on covid-psychology-large.txt at article level

#loading dataset
document_list,titles,word_count_psy=load_data("","covid-psychology-large.txt",'NEW PAPER')

Number of words in text file : 183592
Total Number of Documents: 40

#data preprocessing
clean_text=preprocess_data(document_list)

['international', 'sociology2021', 'author', '2020article', 'reuse', 'guidelines', 'sagepub', 'journals', 'permissionsdoi', '1177', '0268580920948807journals', 'sagepub', 'home', 'isspsychology', 'politics', 'ofcovid', 'misinfodemics', 'peoplebelieve', 'misinfodemics', 'sonia', 'mukhtaruniversity', 'management', 'technology', 'lahore', 'pakistanabstractmisinfodemics', 'related', 'covid', 'have', 'negatively', 'impacted', 'people', 'lives', 'with', 'adversehealth', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicateevidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'risk', 'emerged', 'effects', 'ofmisinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevierabout', 'covid', 'related', 'misinfodemics', 'have', 'been', 'considered', 'reviewed', 'this', 'article', 'thisreview', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects', 'responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'articleshows', 'that', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product', 'apsychological', 'predisposition', 'which', 'either', 'reject', 'information', 'from', 'experts', 'perceive', 'thecrisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations', 'psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'have', 'implications', 'thedevelopment', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health', 'keywordsconspiracy', 'theories', 'covid', 'health', 'communication', 'misinformation', 'mistrust', 'sciencethe', 'mechanisms', 'misinfodemicsin', 'this', 'modern', 'mainstream', 'social', 'media', 'have', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'thus', 'risk', 'misinfodemics', 'surroundingcorresponding', 'author', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'town', 'lahore', 'punjab', '54770', 'pakistan', 'email', 'sonia', 'mukhtar12', 'gmail', 'com948807iss0010', '1177', '0268580920948807international', 'sociologymukhtarresearch', 'article2020article112', 'international', 'sociology', 'covid', 'pandemic', 'even', 'more', 'challenging', 'curb', 'ongoing', 'coronavirus', 'covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modernglobalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origindue', 'unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'duringlockdown', 'quarantine', 'self', 'isolation', 'social', 'distancing', 'virtual', 'communication', 'hasbecome', 'major', 'source', 'interaction', 'holman', '2020', 'covid', 'pandemic', 'outbreak', 'only', 'escalated', 'challenges', 'forhealthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'over', 'world', 'also', 'gained', 'momentum', 'innumerablemisinfodemics', 'mechanisms', 'rumors', 'myths', 'superstitions', 'conspiracy', 'theories', 'claims', 'hoaxes', 'false', 'misinformation', 'fake', 'news', 'polarization', 'mistrust', 'science', 'intimes', 'crisis', 'absence', 'fact', 'checking', 'misinformation', 'misleading', 'content', 'falsecontext', 'manipulated', 'data', 'fabricated', 'material', 'imposter', 'documents', 'disinformation', 'regarding', 'etiology', 'outcomes', 'misplaced', 'prevention', 'disease', 'mukhtar', '2020a', '2020', 'covid', 'pandemic', 'outbreak', 'saturated', 'mainstream', 'media', 'which', 'disseminate', 'information', 'local', 'global', 'scale', 'similarly', 'social', 'media', 'platforms', 'havealso', 'become', 'accessible', 'source', 'information', 'numerous', 'incidents', 'initiated', 'bythese', 'rumors', 'have', 'caused', 'several', 'mishaps', 'across', 'world', 'instance', 'took', 'hisown', 'life', 'after', 'positive', 'diagnosis', 'covid', 'india', 'people', 'have', 'overdosed', 'onthe', 'drug', 'cholorquine', 'after', 'news', 'about', 'effectiveness', 'against', 'covid', 'proliferated', 'nigeria', 'busari', 'adebayo', '2020', '2020', 'there', 'been', 'failure', 'makea', 'distinction', 'between', 'empirically', 'based', 'scientific', 'truths', 'fabricated', 'unconfirmedanti', 'science', 'conspiracy', 'theories', 'among', 'general', 'public', 'such', 'reports', 'reduce', 'legitimacy', 'scientific', 'discoveries', 'regarding', 'cure', 'vaccine', 'covid', 'this', 'canalso', 'create', 'social', 'stigma', 'resulting', 'xenophobia', 'anti', 'chinese', 'sentiment', 'racism', 'marginalization', 'reduced', 'compliance', 'adherence', 'quarantine', 'have', 'adverse', 'healthand', 'psychosocial', 'impacts', 'aguilera', '2020', 'rana', '2020a', '2020b', 'these', 'considerations', 'become', 'even', 'more', 'exacerbated', 'during', 'lockdown', 'leading', 'people', 'fringe', 'ofpopular', 'opinion', 'spend', 'ever', 'more', 'time', 'social', 'media', 'people', 'trying', 'tomake', 'sense', 'their', 'changed', 'lives', 'self', 'proclaimed', 'celebrities', 'self', 'identified', 'socialmedia', 'stars', 'politicians', 'mainstream', 'media', 'public', 'figures', 'propagating', 'theirown', 'subjective', 'interpretations', 'events', 'this', 'situation', 'covid', 'pandemic', 'misinfodemics', 'lederer', '2020', 'scientific', 'studies', 'from', 'previous', 'disease', 'outbreaks', 'have', 'demonstrated', 'that', 'misinformation', 'represents', 'secondary', 'challenge', 'public', 'health', 'efforts', 'controlling', 'epidemic', 'pandemic', 'earnshaw', '2019', 'kalichman', '2009', 'individuals', 'endorsemisinformation', 'about', 'disease', 'less', 'likely', 'follow', 'public', 'health', 'instructions', 'duringthe', 'ebola', 'outbreak', '2014', 'respondents', 'gave', 'credence', 'conspiracy', 'theoriesalleged', 'that', 'they', 'would', 'less', 'likely', 'seek', 'support', 'during', 'disease', 'outbreak', 'national', 'cancer', 'institute', '2020', '2019', 'study', 'montanaro', '2020', 'conducted', 'theus', 'over', 'respondents', 'reported', 'trust', 'medical', 'healthcare', 'professionals', 'contrast', '2020', 'study', 'where', 'majority', 'respondents', 'reported', 'mistrust', 'informationabout', 'covid', 'from', 'current', 'administration', 'mainstream', 'media', 'news', 'outlets', 'york', 'times', '2020', 'mukhtar', '113the', 'psychology', 'misinfodemicsfear', 'adaptive', 'emotion', 'which', 'serves', 'mobilize', 'energy', 'towards', 'potential', 'actualor', 'perceived', 'threat', 'mertens', '2018', 'however', 'when', 'fear', 'excessive', 'then', 'thismay', 'have', 'detrimental', 'effects', 'individual', 'mental', 'health', 'problems', 'like', 'anxiety', 'andphobia', 'community', 'level', 'hoarding', 'panic', 'shopping', 'xenophobia', 'when', 'fear', 'isinsufficient', 'then', 'this', 'also', 'cause', 'harm', 'both', 'individual', 'incompliance', 'nonadherence', 'towards', 'quarantine', 'isolation', 'community', 'reckless', 'implementation', 'ofpolicies', 'that', 'ignores', 'risks', 'socioeconomic', 'infrastructure', 'likewise', 'interpersonalsafety', 'measures', 'mitigate', 'certain', 'threats', 'transmission', 'disease', 'paradoxically', 'enhance', 'fear', 'fear', 'transmission', 'excessive', 'health', 'anxiety', 'psychosocialissues', 'similarly', 'social', 'safety', 'measures', 'lockdowns', 'curfews', 'help', 'control', 'transmission', 'prolonged', 'stringent', 'measures', 'could', 'have', 'negative', 'consequences', 'socioeconomic', 'impact', 'mental', 'health', 'impact', 'efforts', 'curb', 'misinfodemics', 'process', 'have', 'increased', 'because', 'adverseeffects', 'public', 'health', 'communication', 'encourage', 'adoption', 'sustainablepreventive', 'measures', 'manage', 'social', 'physical', 'distancing', 'psychological', 'health', 'andresilience', 'socioeconomic', 'conditions', 'address', 'stigma', 'prejudice', 'discriminationand', 'inequalities', 'mukhtar', '2020b', 'among', 'various', 'factors', 'psychological', 'vulnerability', 'mukhtar', '2020c', 'mukhtar', 'mahmood', '2018', 'propensity', 'follow', 'popularopinion', 'excessive', 'social', 'media', 'presence', 'predisposition', 'anxiety', 'fear', 'lead', 'touncertainty', 'intolerance', 'unpredictability', 'coronavirus', 'brings', 'high', 'levels', 'uncertainty', 'inability', 'cope', 'with', 'uncertainty', 'exponentially', 'higher', 'causing', 'anxiety', 'fear', 'health', 'related', 'worry', 'this', 'situation', 'exacerbated', 'exposure', 'plethoraof', 'information', 'including', 'misinformation', 'disinformation', 'about', 'impendingthreat', 'from', 'mainstream', 'media', 'social', 'media', 'alike', 'rosser', '2019', 'perceived', 'threatinformation', 'elevates', 'fear', 'repeated', 'engagement', 'with', 'trauma', 'related', 'media', 'contentfor', 'several', 'hours', 'daily', 'culminates', 'acute', 'stress', 'emotional', 'distress', 'consequentlywill', 'either', 'increase', 'fear', 'virus', 'cause', 'insensitivity', 'towards', 'course', 'identification', 'monitoring', 'internet', 'centrifugal', 'clarification', 'filter', 'theaccuracy', 'content', 'become', 'more', 'challenging', 'increasing', 'number', 'people', 'relyon', 'social', 'media', 'platforms', 'challenging', 'problems', 'misinformation', 'disinformation', 'conspiracy', 'theories', 'social', 'media', 'they', 'emulsify', 'into', 'false', 'misleading', 'click', 'bait', 'content', 'world', 'health', 'organization', 'launched', 'mythbuster', 'feature', 'website', 'countermeasure', 'spread', 'unauthenticnews', 'social', 'media', 'people', 'reported', 'highest', 'media', 'exposure', 'reported', 'higheracute', 'stress', 'holman', '2020', 'media', 'exposure', 'accumulates', 'ever', 'emerging', 'threatsand', 'repeated', 'exposure', 'these', 'events', 'increase', 'symptoms', 'distress', 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'andpersonal', 'reflection', 'that', 'allow', 'recognition', 'control', 'thenegative', 'emotions', 'that', 'death', 'dying', 'arouse', 'consequently', 'they', 'also', 'allow', 'people', 'focus', 'attention', 'life', 'through', 'thedissolution', 'taboos', 'related', 'these', 'topics', 'they', 'have', 'becomeaware', 'learned', 'recognize', 'negative', 'emotions', 'thatoccur', 'situations', 'related', 'death', 'finally', 'positivity', 'thecourse', 'substantially', 'derived', 'from', 'focus', 'life', 'onthe', 'recognition', 'importance', 'general', 'participantsrecognized', 'need', 'adequate', 'preparation', 'regarding', 'thetopics', 'covered', 'course', 'only', 'live', 'better', 'dealwith', 'these', 'issues', 'more', 'serenely', 'also', 'their', 'future', 'healthprofessionals', 'participants', 'therefore', 'expressed', 'only', 'positivepsychological', 'educational', 'effects', 'valuing', 'much', 'thecourse', 'allowed', 'them', 'better', 'manage', 'social', 'traumafrontiers', 'public', 'health', 'frontiersin', 'april', '2021', 'volume', 'article', '625756testoni', 'psychology', 'students', 'perceptions', 'covid', '19figure', 'findings', 'qualitative', 'data', 'analysis', 'main', 'themes', 'their', 'relationships', 'caused', 'covid', 'this', 'appears', 'line', 'with', 'otherliterature', 'especially', 'studies', 'involving', 'also', 'other', 'healthcarestudents', 'well', 'which', 'have', 'confirmed', 'important', 'toimplement', 'death', 'education', 'pathways', 'university', 'students', 'especially', 'those', 'studying', 'become', 'health', 'professionals', 'present', 'results', 'highlighted', 'well', 'conclusionsthe', 'present', 'study', 'highlighted', 'death', 'education', 'course', 'conducted', 'during', 'first', 'phase', 'covid', 'pandemic', 'initaly', 'helped', 'students', 'become', 'more', 'aware', 'issues', 'addressedthrough', 'contents', 'dialogues', 'that', 'encouraged', 'participants', 'todevelop', 'their', 'critical', 'thinking', 'allowed', 'them', 'rediscoverthe', 'value', 'reflection', 'sense', 'limit', 'finitude', 'life', 'andthe', 'different', 'representations', 'death', 'since', 'these', 'meanings', 'areusually', 'taken', 'granted', 'despite', 'general', 'lack', 'awarenessabout', 'representations', 'death', 'possibility', 'freely', 'reflectupon', 'discuss', 'these', 'issues', 'offered', 'course', 'renderedit', 'therefore', 'significant', 'tool', 'support', 'participants', 'duringthe', 'confusing', 'highly', 'dramatic', 'period', 'pandemic', 'related', 'this', 'research', 'results', 'showed', 'also', 'that', 'coursehelped', 'participants', 'consider', 'religious', 'spiritual', 'aspectcorresponding', 'distal', 'defenses', 'indicated', 'indeed', 'line', 'with', 'perspective', 'religious', 'spiritualvision', 'mitigates', 'concerns', 'that', 'arise', 'from', 'awareness', 'ofdeath', 'allows', 'people', 'focus', 'their', 'attention', 'everyday', 'lifewithout', 'being', 'assailed', 'death', 'anxiety', 'another', 'fundamentalelement', 'that', 'emerged', 'from', 'research', 'been', 'therediscovered', 'importance', 'funeral', 'rites', 'maintain', 'senseof', 'community', 'especially', 'while', 'facing', 'extremely', 'painful', 'historicalperiods', 'significantly', 'characterized', 'grief', 'study', 'highlightedindeed', 'concrete', 'need', 'address', 'these', 'problems', 'linkthem', 'wider', 'existential', 'issues', 'such', 'ability', 'improvereflection', 'meaning', 'life', 'address', 'sense', 'limit', 'these', 'results', 'confirm', 'once', 'again', 'effectiveness', 'reflectionon', 'death', 'dying', 'critical', 'periods', 'such', 'covid', '19pandemic', 'because', 'permits', 'awareness', 'unconscious', 'aspectsthat', 'characterize', 'terror', 'death', 'gives', 'them', 'cultural', 'andrelative', 'dimensions', 'this', 'especially', 'important', 'healthcareprofessionals', 'already', 'find', 'generally', 'difficult', 'ratherdistressing', 'manage', 'life', 'patient', 'lackof', 'proper', 'training', 'these', 'aspects', 'therefore', 'haveto', 'face', 'this', 'pandemic', 'period', 'additional', 'psychologicalburden', 'dealing', 'with', 'even', 'higher', 'mortality', 'salience', 'lack', 'ofproper', 'personal', 'professional', 'tools', 'elaborate', 'breakthe', 'silence', 'taboo', 'that', 'generally', 'surrounds', 'experience', 'ofdeath', 'even', 'them', 'considering', 'this', 'covid', 'pandemic', 'extremely', 'high', 'number', 'deaths', 'their', 'massive', 'burden', 'forhealthcare', 'professionals', 'actively', 'deal', 'with', 'them', 'dailybasis', 'represent', 'significant', 'public', 'health', 'challenge', 'whichmore', 'more', 'attention', 'should', 'given', 'near', 'future', 'given', 'current', 'study', 'findings', 'therefore', 'introductionand', 'increase', 'death', 'education', 'pathways', 'university', 'coursescould', 'great', 'importance', 'especially', 'present', 'situationfrontiers', 'public', 'health', 'frontiersin', 'april', '2021', 'volume', 'article', '625756testoni', 'psychology', 'students', 'perceptions', 'covid', '19of', 'covid', 'pandemic', 'they', 'could', 'indeed', 'help', 'young', 'adultsobtain', 'proper', 'resources', 'personal', 'skills', 'they', 'need', 'toadequately', 'face', 'this', 'period', 'intense', 'uncertainty', 'mortalitysalience', 'other', 'future', 'challenge', 'there', 'might', 'store', 'especially', 'healthcare', 'students', 'like', 'psychologists', 'example', 'will', 'need', 'able', 'properly', 'address', 'these', 'themes', 'withtheir', 'patients', 'near', 'future', 'proper', 'death', 'education', 'coursesshould', 'therefore', 'implemented', 'keeping', 'mind', 'need', 'toallow', 'students', 'openly', 'discuss', 'without', 'prejudices', 'fear', 'thetheme', 'death', 'human', 'vulnerability', 'express', 'relatedemotions', 'through', 'mean', 'they', 'feel', 'could', 'helpful', 'limits', 'future', 'perspectivesthe', 'research', 'reports', 'encouraging', 'results', 'that', 'demonstrate', 'thepositive', 'effects', 'death', 'education', 'however', 'some', 'limitationsshould', 'taken', 'into', 'account', 'including', 'inability', 'generalizethe', 'results', 'given', 'specificity', 'conditions', 'concerning', 'this', 'future', 'research', 'should', 'focus', 'significantly', 'broaden', 'numberof', 'participants', 'addition', 'this', 'idea', 'longitudinalstudy', 'considered', 'approach', 'discussionof', 'existential', 'issues', 'addressed', 'actually', 'changed', 'andkept', 'stable', 'over', 'time', 'moreover', 'since', 'research', 'participantswere', 'mostly', 'female', 'even', 'though', 'psychology', 'students', 'areusually', 'more', 'female', 'than', 'male', 'future', 'research', 'could', 'exploremore', 'precisely', 'more', 'male', 'psychology', 'students', 'point', 'view', 'comparing', 'female', 'students', 'other', 'interestingaspects', 'that', 'could', 'investigated', 'future', 'studies', 'could', 'also', 'bethe', 'perspective', 'medical', 'nursing', 'social', 'services', 'students', 'well', 'being', 'conducted', 'other', 'cultural', 'contexts', 'sincecomparing', 'psychology', 'students', 'perspectives', 'point', 'viewof', 'other', 'healthcare', 'social', 'services', 'professionals', 'could', 'beparticularly', 'interesting', 'informative', 'data', 'availability', 'statementthe', 'data', 'supporting', 'conclusions', 'thisarticle', 'will', 'made', 'available', 'authors', 'withoutundue', 'reservation', 'ethics', 'statementthe', 'studies', 'involving', 'human', 'participants', 'werereviewed', 'approved', 'ethical', 'committee', 'forthe', 'psychological', 'research', 'university', 'ofpadua', '57bc2002fdf5cbd4292f5f86aa077f23', 'thepatients', 'participants', 'provided', 'their', 'written', 'informed', 'consent', 'toparticipate', 'this', 'study', 'author', 'contributionsit', 'contributed', 'research', 'design', 'project', 'planning', 'supervision', 'research', 'analysis', 'texts', 'methodology', 'article', 'writing', 'contributed', 'project', 'planning', 'analysis', 'texts', 'article', 'writing', 'contributed', 'tothe', 'interviews', 'analysis', 'texts', 'contributed', 'thesupervision', 'cooperation', 'organization', 'teamwork', 'andwriting', 'contributed', 'analysis', 'texts', 'cooperation', 'organization', 'team', 'work', 'contributed', 'tothe', 'writing', 'authors', 'contributed', 'article', 'approvedthe', 'submitted', 'version', 'acknowledgmentswe', 'thank', 'davide', 'viezzoli', 'marco', 'antonellini', 'alice', 'culcasi', 'andgianmarco', 'biancalani', 'help', 'some', 'critical', 'phases', 'ofthe', 'work']

# LSA Model
number_of_topics=25
words=word_count_psy
model_psy=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model_psy.show_topic(0, topn=words))#dict and encoding matrix values

#These variables are for further experimentation in vertical comparission mantel test
model_psy_articles=create_gensim_lsa_model(clean_text,number_of_topics,words)

#dictionary of top 5 words
dict(model_psy.show_topic(0,topn=5))

{'covid': 0.2898611145723357,
 '2020': 0.2862536527658803,
 'psycholog': 0.28058846178894215,
 'student': 0.2670404119683266,
 'pandem': 0.19867186971131373}

#wordcloud of top 10 words from lsa
from wordcloud import WordCloud
text = dict(model_psy.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph22.png')

#cosine similarity matrix of top 5 words

a=pd.DataFrame()

for i in range(number_of_topics):
    words_psy =dict(model_psy.show_topic(i, topn=words))
    b=pd.DataFrame(words_psy,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_psy.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled25.png')

cosine-similarity
               covid       2020  psycholog    student     pandem
covid              1   0.305279   0.150688  0.0723404    0.28409
2020        0.305279          1   0.138506 -0.0647919   0.291954
psycholog   0.150688   0.138506          1  0.0712322   0.128405
student    0.0723404 -0.0647919  0.0712322          1  0.0743237
pandem       0.28409   0.291954   0.128405  0.0743237          1

#networkx garph for top 5 words
df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='r',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig25.png')

# defining mantel test function
def mantel_test(n):
    #all words from corpus
    words_science = dict(model_science.show_topic(0, topn=word_count_science))
    words_psy = dict(model_psy.show_topic(0, topn=word_count_psy))
    #top words from the corpus
    top_words_science = dict(model_science.show_topic(0, topn=n)) 
    top_words_psy = dict(model_psy.show_topic(0,topn=n)) 

    #Finding out the weights of top 5 law words in covid science and updating their weights w.r.t psychology dataset 
    c={} 
    for key in top_words_science:
        try:
            c[key] = words_psy[key]
        except KeyError:
            c[key] = 0

    top_words_psy.update(c)
    #Finding out the weights of top 5 law words in covid psychology and updating their weights w.r.t covid science dataset 
    b={}
    for key in top_words_psy:
        try:
            b[key] = words_science[key]
        except KeyError:
            b[key] = 0

    b.update(top_words_science)
    top_words_science=b

    #pairwise distance for top 10 words in covid science
    a=pd.DataFrame()

    for i in range(number_of_topics):
        words_from_science =dict(model_science.show_topic(i, topn=word_count_science))
        b=pd.DataFrame(words_from_science,index=[i])
        a=a.append(b)

    a=a.transpose()

    df = pd.DataFrame(columns=list(top_words_science.keys()), 
                      index = list(top_words_science.keys()))

    l = list(top_words_science.keys())

    print('Pairwise-distance')
    for i in l:
        for j in l:
            try:
                matrix=np.array([a.loc[i],a.loc[j]])
            except KeyError:
                matrix=np.zeros((2,3))
            n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
            df.loc[[i],[j]] = n[0,1]
    print(df)
    df1 = df.to_numpy()
    #pairwise distace of top 10 words in psychology dataset
    a=pd.DataFrame()

    for i in range(number_of_topics):
        words_from_psy =dict(model_psy.show_topic(i, topn=word_count_psy))
        b=pd.DataFrame(words_from_psy,index=[i])
        a=a.append(b)

    a=a.transpose()

    df = pd.DataFrame(columns=list(top_words_psy.keys()), 
                      index = list(top_words_psy.keys()))

    l = list(top_words_psy.keys())
    print('Pairwise-distance')
    for i in l:
        for j in l:
            try:
                matrix=np.array([a.loc[i],a.loc[j]])
            except KeyError:
                matrix=np.zeros((2,3))
            n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
            df.loc[[i],[j]] = n[0,1]
    print(df)
    df2 = df.to_numpy()
    

    dist1 = list(df1[np.triu_indices(len(top_words_science), k = 1)])

    

    dist2 = list(df2[np.triu_indices(len(top_words_psy), k = 1)])

    return mantel.test(dist1, dist2, perms=10000, method='pearson', tail='upper')

#mantel test for 10 word network
mantel_test(5)

Pairwise-distance
              covid      2020 psycholog      student       pandem      imag  \
covid             0  0.702842  0.716739     0.918356     0.680495   1.01902   
2020       0.702842         0  0.290158     0.653557     0.380614  0.861552   
psycholog  0.716739  0.290158         0      0.55126     0.276821  0.806033   
student    0.918356  0.653557   0.55126  1.05367e-08     0.637377  0.973622   
pandem     0.680495  0.380614  0.276821     0.637377  5.26836e-09  0.875786   
imag        1.01902  0.861552  0.806033     0.973622     0.875786         0   
model       1.03673  0.786081  0.784766     0.963188     0.807124   1.16537   
use         0.74204  0.544595  0.460391     0.676651     0.548179  0.884217   
data       0.952954  0.856791  0.763671     0.961593     0.823599    1.1404   

                 model       use      data  
covid          1.03673   0.74204  0.952954  
2020          0.786081  0.544595  0.856791  
psycholog     0.784766  0.460391  0.763671  
student       0.963188  0.676651  0.961593  
pandem        0.807124  0.548179  0.823599  
imag           1.16537  0.884217    1.1404  
model      1.49012e-08  0.874917   1.09205  
use           0.874917         0   0.88476  
data           1.09205   0.88476         0  
Pairwise-distance
                 covid      2020    psycholog   student    pandem  \
covid      1.49012e-08  0.741988     0.722493  0.915764  0.592669   
2020          0.741988         0     0.806135   1.05108  0.677856   
psycholog     0.722493  0.806135  1.05367e-08  0.898322  0.622704   
student       0.915764   1.05108     0.898322         0   0.82732   
pandem        0.592669  0.677856     0.622704   0.82732         0   
imag          0.570371  0.685021     0.544262   0.76015  0.386734   
model         0.569918  0.655596     0.579156  0.781989  0.420834   
use           0.609241  0.773019     0.570648  0.814629  0.485171   
data          0.592907  0.783642     0.628248  0.829132  0.467766   

                  imag     model          use      data  
covid         0.570371  0.569918     0.609241  0.592907  
2020          0.685021  0.655596     0.773019  0.783642  
psycholog     0.544262  0.579156     0.570648  0.628248  
student        0.76015  0.781989     0.814629  0.829132  
pandem        0.386734  0.420834     0.485171  0.467766  
imag       6.58545e-10  0.152036     0.322383  0.311478  
model         0.152036         0     0.292563  0.302549  
use           0.322383  0.292563  7.45058e-09  0.233008  
data          0.311478  0.302549     0.233008         0  

(-0.4264559490750131, 0.8876, -1.2680325703061341)

#mantel test for 20 word network
mantel_test(10)

Pairwise-distance
              covid      2020 psycholog      student       pandem     train  \
covid             0  0.702842  0.716739     0.918356     0.680495    0.6956   
2020       0.702842         0  0.290158     0.653557     0.380614  0.398691   
psycholog  0.716739  0.290158         0      0.55126     0.276821   0.32078   
student    0.918356  0.653557   0.55126  1.05367e-08     0.637377  0.660347   
pandem     0.680495  0.380614  0.276821     0.637377  5.26836e-09  0.444486   
train        0.6956  0.398691   0.32078     0.660347     0.444486         0   
social     0.835514   0.48014   0.37424     0.670737      0.44328  0.458718   
health     0.694141  0.289767  0.196136      0.55408     0.280294  0.385114   
time       0.751346  0.310403  0.322936     0.660202      0.43743  0.468233   
group      0.947395   0.60563  0.585684     0.800901     0.652223  0.678309   
imag        1.01902  0.861552  0.806033     0.973622     0.875786  0.735491   
model       1.03673  0.786081  0.784766     0.963188     0.807124  0.818487   
use         0.74204  0.544595  0.460391     0.676651     0.548179  0.571733   
data       0.952954  0.856791  0.763671     0.961593     0.823599  0.839749   
dataset    0.953491   0.67662  0.584592     0.809477     0.667077  0.684232   
infect     0.919167  0.660977  0.631905     0.867832     0.681512  0.664693   
base       0.775466  0.512771  0.356224     0.678868     0.472576   0.50209   
test       0.881347  0.652149  0.610581     0.833729     0.656102  0.651366   
perform    0.786427  0.484207  0.379082     0.704291     0.493605  0.430541   

                social       health         time     group      imag  \
covid         0.835514     0.694141     0.751346  0.947395   1.01902   
2020           0.48014     0.289767     0.310403   0.60563  0.861552   
psycholog      0.37424     0.196136     0.322936  0.585684  0.806033   
student       0.670737      0.55408     0.660202  0.800901  0.973622   
pandem         0.44328     0.280294      0.43743  0.652223  0.875786   
train         0.458718     0.385114     0.468233  0.678309  0.735491   
social     7.45058e-09     0.398087     0.427746  0.744925  0.915651   
health        0.398087  5.26836e-09     0.353259  0.625724  0.850805   
time          0.427746     0.353259  7.45058e-09  0.649916  0.886331   
group         0.744925     0.625724     0.649916         0  0.981023   
imag          0.915651     0.850805     0.886331  0.981023         0   
model         0.880212     0.815866     0.836392   1.01086   1.16537   
use           0.592859     0.483053     0.482778  0.733082  0.884217   
data          0.845042     0.796967      0.77724  0.983044    1.1404   
dataset        0.67805     0.622512     0.674827  0.864814  0.899592   
infect        0.735539     0.675218     0.714383  0.860848   1.04112   
base           0.52881     0.418641     0.489037   0.71373  0.887181   
test          0.475159     0.651109      0.66315  0.653203    1.0585   
perform       0.534512     0.426711     0.506194   0.71898  0.874763   

                 model       use      data      dataset    infect      base  \
covid          1.03673   0.74204  0.952954     0.953491  0.919167  0.775466   
2020          0.786081  0.544595  0.856791      0.67662  0.660977  0.512771   
psycholog     0.784766  0.460391  0.763671     0.584592  0.631905  0.356224   
student       0.963188  0.676651  0.961593     0.809477  0.867832  0.678868   
pandem        0.807124  0.548179  0.823599     0.667077  0.681512  0.472576   
train         0.818487  0.571733  0.839749     0.684232  0.664693   0.50209   
social        0.880212  0.592859  0.845042      0.67805  0.735539   0.52881   
health        0.815866  0.483053  0.796967     0.622512  0.675218  0.418641   
time          0.836392  0.482778   0.77724     0.674827  0.714383  0.489037   
group          1.01086  0.733082  0.983044     0.864814  0.860848   0.71373   
imag           1.16537  0.884217    1.1404     0.899592   1.04112  0.887181   
model      1.49012e-08  0.874917   1.09205     0.972214  0.949947  0.859456   
use           0.874917         0   0.88476     0.672032  0.819454  0.529919   
data           1.09205   0.88476         0     0.909099   1.02911  0.789871   
dataset       0.972214  0.672032  0.909099  1.05367e-08  0.938299  0.720105   
infect        0.949947  0.819454   1.02911     0.938299         0  0.678111   
base          0.859456  0.529919  0.789871     0.720105  0.678111         0   
test          0.996068  0.691209  0.997764     0.816099  0.868632   0.70621   
perform       0.768261  0.469848  0.825144     0.708714  0.789889  0.476341   

               test   perform  
covid      0.881347  0.786427  
2020       0.652149  0.484207  
psycholog  0.610581  0.379082  
student    0.833729  0.704291  
pandem     0.656102  0.493605  
train      0.651366  0.430541  
social     0.475159  0.534512  
health     0.651109  0.426711  
time        0.66315  0.506194  
group      0.653203   0.71898  
imag         1.0585  0.874763  
model      0.996068  0.768261  
use        0.691209  0.469848  
data       0.997764  0.825144  
dataset    0.816099  0.708714  
infect     0.868632  0.789889  
base        0.70621  0.476341  
test              0  0.717819  
perform    0.717819         0  
Pairwise-distance
                 covid      2020    psycholog   student    pandem     train  \
covid      1.49012e-08  0.741988     0.722493  0.915764  0.592669  0.755341   
2020          0.741988         0     0.806135   1.05108  0.677856  0.870708   
psycholog     0.722493  0.806135  1.05367e-08  0.898322  0.622704  0.674962   
student       0.915764   1.05108     0.898322         0   0.82732  0.859899   
pandem        0.592669  0.677856     0.622704   0.82732         0  0.546224   
train         0.755341  0.870708     0.674962  0.859899  0.546224         0   
social         0.79574  0.842492     0.833994  0.940387  0.676052  0.798289   
health        0.836536  0.794454     0.793785   1.04724  0.635648  0.831146   
time          0.615218  0.644129     0.561415  0.812616  0.470924  0.533845   
group         0.925229   1.00092     0.934349   1.10568  0.881893  0.909853   
imag          0.570371  0.685021     0.544262   0.76015  0.386734   0.50608   
model         0.569918  0.655596     0.579156  0.781989  0.420834  0.525861   
use           0.609241  0.773019     0.570648  0.814629  0.485171  0.610886   
data          0.592907  0.783642     0.628248  0.829132  0.467766  0.618134   
dataset       0.570268  0.677879     0.538763  0.758604  0.392452  0.499391   
infect        0.548458  0.687215     0.579617   0.74807  0.426181  0.540052   
base          0.568919  0.686644     0.543193  0.779082  0.361024  0.484295   
test          0.527961  0.695333     0.593835  0.769567  0.422094  0.521226   
perform       0.608586  0.711845     0.572644  0.790783  0.427698  0.547594   

             social    health         time     group         imag     model  \
covid       0.79574  0.836536     0.615218  0.925229     0.570371  0.569918   
2020       0.842492  0.794454     0.644129   1.00092     0.685021  0.655596   
psycholog  0.833994  0.793785     0.561415  0.934349     0.544262  0.579156   
student    0.940387   1.04724     0.812616   1.10568      0.76015  0.781989   
pandem     0.676052  0.635648     0.470924  0.881893     0.386734  0.420834   
train      0.798289  0.831146     0.533845  0.909853      0.50608  0.525861   
social            0  0.872135     0.602903  0.871491     0.561679  0.543162   
health     0.872135         0     0.693341   1.03149     0.653583  0.682513   
time       0.602903  0.693341  5.26836e-09   0.78786     0.251524  0.202602   
group      0.871491   1.03149      0.78786         0     0.770523  0.795192   
imag       0.561679  0.653583     0.251524  0.770523  6.58545e-10  0.152036   
model      0.543162  0.682513     0.202602  0.795192     0.152036         0   
use        0.605968  0.702358     0.369752  0.838988     0.322383  0.292563   
data       0.650201  0.700384     0.389576  0.842226     0.311478  0.302549   
dataset    0.569877  0.651757     0.248456  0.762329    0.0406396  0.141868   
infect     0.565671  0.656363     0.310996  0.790076     0.162348  0.206416   
base       0.551976  0.620955      0.25983  0.796315     0.119009   0.17623   
test       0.589158  0.670902     0.250695  0.785098     0.147665  0.135569   
perform    0.598756  0.690647     0.260955  0.796435     0.203783  0.254205   

                   use      data      dataset    infect      base  \
covid         0.609241  0.592907     0.570268  0.548458  0.568919   
2020          0.773019  0.783642     0.677879  0.687215  0.686644   
psycholog     0.570648  0.628248     0.538763  0.579617  0.543193   
student       0.814629  0.829132     0.758604   0.74807  0.779082   
pandem        0.485171  0.467766     0.392452  0.426181  0.361024   
train         0.610886  0.618134     0.499391  0.540052  0.484295   
social        0.605968  0.650201     0.569877  0.565671  0.551976   
health        0.702358  0.700384     0.651757  0.656363  0.620955   
time          0.369752  0.389576     0.248456  0.310996   0.25983   
group         0.838988  0.842226     0.762329  0.790076  0.796315   
imag          0.322383  0.311478    0.0406396  0.162348  0.119009   
model         0.292563  0.302549     0.141868  0.206416   0.17623   
use        7.45058e-09  0.233008     0.310807  0.378787  0.317153   
data          0.233008         0     0.311237  0.358787  0.282989   
dataset       0.310807  0.311237  1.64636e-10  0.171029  0.135016   
infect        0.378787  0.358787     0.171029         0  0.192282   
base          0.317153  0.282989     0.135016  0.192282         0   
test          0.353242   0.32685     0.146599  0.193608  0.186858   
perform       0.379788  0.383873     0.200365  0.267479  0.224621   

                  test      perform  
covid         0.527961     0.608586  
2020          0.695333     0.711845  
psycholog     0.593835     0.572644  
student       0.769567     0.790783  
pandem        0.422094     0.427698  
train         0.521226     0.547594  
social        0.589158     0.598756  
health        0.670902     0.690647  
time          0.250695     0.260955  
group         0.785098     0.796435  
imag          0.147665     0.203783  
model         0.135569     0.254205  
use           0.353242     0.379788  
data           0.32685     0.383873  
dataset       0.146599     0.200365  
infect        0.193608     0.267479  
base          0.186858     0.224621  
test       2.63418e-09     0.254417  
perform       0.254417  3.72529e-09  

(-0.2857142874137023, 0.9018, -1.2970979073940034)

2. 2-3 paragraphs

LSA on covid-science.txt at 2-3 paragraph level

#loading dataset
def load_data(path,file_name):
    """
    Input  : path and file_name
    Purpose: loading text file
    Output : list of paragraphs/documents and
             title(initial 100 words considred as title of document)
    """
    documents_list = []
    titles=[]
    document = ''
    i=0
    with open( os.path.join(path, file_name) ,"r",encoding="utf8") as fin:
        data = fin.read()
        words = data.split()
        fin.seek(0)
        print('Number of words in text file :', len(words))
        for word in words:
                i=i+1
                document = document+word+' '
                if i == 200:
                    documents_list.append(document)
                    document,i = '',0
                    if len(documents_list) == 2:
                        break
    print("Total Number of Documents:",len(documents_list))
    #titles.append( text[0:min(len(text),100)] )
    return documents_list,titles,len(words)

document_list,titles,word_count_science=load_data("","covid-science-large.txt")

Number of words in text file : 274941
Total Number of Documents: 2

#data preprocessing
clean_text=preprocess_data(document_list)

['covidgr', 'dataset', 'covid', 'sdnet', 'methodology', 'predicting', 'covid', 'based', 'chest', 'images', 'tabik', 'gómez', 'ríos', 'martín', 'rodríguez', 'sevillano', 'garcía', 'area', 'charte', 'guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'herrera', 'abstract', 'currently', 'coronavirus', 'disease', 'covid', 'most', 'infectious', 'diseases', '21st', 'century', 'diagnosed', 'using', 'testing', 'scans', 'chest', 'images', 'computed', 'tomography', 'scanners', 'testing', 'available', 'most', 'medical', 'centers', 'hence', 'many', 'cases', 'images', 'become', 'most', 'time', 'cost', 'effective', 'tool', 'assisting', 'clinicians', 'making', 'decisions', 'deep', 'learning', 'neural', 'networks', 'have', 'great', 'potential', 'building', 'covid', 'triage', 'systems', 'detecting', 'covid', 'patients', 'especially', 'patients', 'with', 'severity', 'unfortunately', 'current', 'databases', 'allow', 'building', 'such', 'systems', 'they', 'highly', 'heterogeneous', 'biased', 'towards', 'severe', 'cases', 'this', 'article', 'threefold', 'demystify', 'high', 'sensitivities', 'achieved', 'most', 'recent', 'covid', 'classification', 'models', 'under', 'close', 'collaboration', 'with', 'hospital', 'universitario', 'clínico', 'cecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'that', 'includes', 'levels', 'manuscript', 'received', 'september', '2020', 'revised', 'october', '2020', 'accepted', 'november', '2020', 'date']
['publication', 'november', '2020', 'date', 'current', 'version', 'december', '2020', 'this', 'work', 'supported', 'project', 'deepscop', 'ayudas', 'fundación', 'bbva', 'equipos', 'investigación', 'científica', 'data', '2018', 'covid19_rx', 'ayudas', 'fundación', 'bbva', 'equipos', 'investigación', 'científica', 'sars', 'covid', '2020', 'spanish', 'ministry', 'science', 'technology', 'under', 'project', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 'cajal', 'programme', '2015', '18136', 'gómez', 'ríos', 'supported', 'programme', 'fpu16', '04765', 'charte', 'supported', 'programme', 'fpu17', '04069', 'suárez', 'supported', 'programme', 'fpu18', '05989', 'supported', 'european', 'research', 'council', 'grant', 'agreement', '647038', 'biodesert', 'this', 'project', 'approved', 'provincial', 'research', 'ethics', 'committee', 'granada', 'corresponding', 'author', 'siham', 'tabik', 'tabik', 'gómez', 'ríos', 'sevillano', 'garcía', 'charte', 'suárez', 'luengo', 'herrera', 'with', 'andalusian', 'research', 'institute', 'data', 'science', 'computational', 'intelligence', 'university', 'granada', '18071', 'granada', 'spain', 'mail', 'siham', 'anabelgrios', 'decsai', 'isega24ivan', 'gmail', 'fdavidcl', 'jlsuarezdiaz', 'julianlm', 'decsai', 'herrera', 'decsai', 'martín', 'rodríguez', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'with', 'hospital', 'universitario', 'clínico', 'cecilio', 'granada', '36310', 'spain', 'mail', 'joseluismartin', 'hotmail', 'valerogonzalez', 'yahoo', 'pgvillanova', 'gmail', 'euolm', 'yahoo', 'area', 'with', 'atlanttic', 'research', 'center']

# LSA Model
number_of_topics=2
words_nscience=len(clean_text[0])+len(clean_text[1])
model_science=create_gensim_lsa_model(clean_text,number_of_topics,words_nscience)
words_science =dict(model_science.show_topic(0, topn=words))##dict and encoding matrix values

#These variables are for further experimentation in vertical comparission mantel test
model_science_p=create_gensim_lsa_model(clean_text,number_of_topics,words_nscience)

#dictionary of top 5 word from lsa
dict(model_science.show_topic(0, topn=5))

{'support': -0.3038609553429639,
 '2020': -0.23522191481735827,
 'granada': -0.23033778261060098,
 'covid': -0.2172263668489119,
 'research': -0.2025739702286426}

#word cloud of top 10 words from lsa
from wordcloud import WordCloud
text = dict(model_science.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph23.png')

#cosine similarity matrix of top 5 words
a=pd.DataFrame()

for i in range(number_of_topics):
    words_science =dict(model_science.show_topic(i, topn = words_nscience))
    b=pd.DataFrame(words_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_science.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled26.png')

cosine-similarity
           support      2020   granada     covid  research
support          1  0.524928  0.955931 -0.115612         1
2020      0.524928         1  0.751685  0.784751  0.524928
granada   0.955931  0.751685         1  0.181107  0.955931
covid    -0.115612  0.784751  0.181107         1 -0.115612
research         1  0.524928  0.955931 -0.115612         1

#networkx graph for top 5 words
df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='green',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig26.png')

LSA of covid-psycology.txt at 2-3 paragraph level

#data loading
document_list,titles,word_count_psy=load_data("","covid-psychology-large.txt")

Number of words in text file : 183592
Total Number of Documents: 2

#data preprocessing
clean_text=preprocess_data(document_list)

['international', 'sociology', '2021', 'author', '2020', 'article', 'reuse', 'guidelines', 'sagepub', 'journals', 'permissions', '1177', '0268580920948807', 'journals', 'sagepub', 'home', 'psychology', 'politics', 'covid', 'misinfodemics', 'people', 'believe', 'misinfodemics', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'lahore', 'pakistan', 'abstract', 'misinfodemics', 'related', 'covid', 'have', 'negatively', 'impacted', 'people', 'lives', 'with', 'adverse', 'health', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicate', 'evidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'risk', 'emerged', 'effects', 'misinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevier', 'about', 'covid', 'related', 'misinfodemics', 'have', 'been', 'considered', 'reviewed', 'this', 'article', 'this', 'review', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects', 'responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'article', 'shows', 'that', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product', 'psychological', 'predisposition', 'which', 'either', 'reject', 'information', 'from', 'experts', 'perceive', 'crisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations', 'psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'have', 'implications', 'development', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health', 'keywords', 'conspiracy', 'theories', 'covid']
['health', 'communication', 'misinformation', 'mistrust', 'science', 'mechanisms', 'misinfodemics', 'this', 'modern', 'mainstream', 'social', 'media', 'have', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'thus', 'risk', 'misinfodemics', 'surrounding', 'corresponding', 'author', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'town', 'lahore', 'punjab', '54770', 'pakistan', 'email', 'sonia', 'mukhtar12', 'gmail', '948807iss0010', '1177', '0268580920948807international', 'sociologymukhtar', 'research', 'article2020', 'article', 'international', 'sociology', 'covid', 'pandemic', 'even', 'more', 'challenging', 'curb', 'ongoing', 'coronavirus', 'covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modern', 'globalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origin', 'unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'during', 'lockdown', 'quarantine', 'self', 'isolation', 'social', 'distancing', 'virtual', 'communication', 'become', 'major', 'source', 'interaction', 'holman', '2020', 'covid', 'pandemic', 'outbreak', 'only', 'escalated', 'challenges', 'healthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'over', 'world', 'also', 'gained', 'momentum', 'innumerable', 'misinfodemics', 'mechanisms', 'rumors', 'myths', 'superstitions', 'conspiracy', 'theories', 'claims', 'hoaxes', 'false', 'misinformation', 'fake', 'news', 'polarization', 'mistrust', 'science', 'times', 'crisis', 'absence', 'fact', 'checking', 'misinformation', 'misleading', 'content', 'false', 'context', 'manipulated', 'data']

# LSA Model
number_of_topics=2
words_npsy=len(clean_text[0])+len(clean_text[1])
model_psy=create_gensim_lsa_model(clean_text,number_of_topics,words_npsy)
words_from_psychology =dict(model_psy.show_topic(0, topn=words))##dict and encoding matrix values

#These variables are for further experimentation in vertical comparission mantel test
model_psy_p = create_gensim_lsa_model(clean_text,number_of_topics,words_npsy)

#dictionary of top 5 words
dict(model_psy.show_topic(0, topn=5))

{'misinfodem': 0.49362666559416474,
 'covid': 0.3571916107745928,
 'articl': 0.21002057347124553,
 'mechan': 0.1471710373033481,
 'health': 0.14717103730334802}

#word cloud of top 10 words
from wordcloud import WordCloud
text = dict(model_psy.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph24.png')

# cosine similarity matrix of top 5 words

a=pd.DataFrame()

for i in range(number_of_topics):
    words_psy =dict(model_psy.show_topic(i, topn=words_npsy))
    b=pd.DataFrame(words_psy,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_psy.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled27.png')

cosine-similarity
           misinfodem     covid    articl    mechan    health
misinfodem          1  0.977431  0.994116  0.761706  0.761706
covid        0.977431         1  0.948797  0.881393  0.881393
articl       0.994116  0.948797         1  0.687043  0.687043
mechan       0.761706  0.881393  0.687043         1         1
health       0.761706  0.881393  0.687043         1         1

#Networkx graph of top 5 words
df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='r',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig27.png')

#mantel test for 10 word network
mantel_test(5)

Pairwise-distance
           misinfodem        covid    articl mechan health   support  \
misinfodem          0            0         0      0      0         0   
covid               0  7.45058e-09  0.360337      0      0  0.608033   
articl              0     0.360337         0      0      0  0.404283   
mechan              0            0         0      0      0         0   
health              0            0         0      0      0         0   
support             0     0.608033  0.404283      0      0         0   
2020                0     0.282616  0.208896      0      0  0.327071   
granada             0     0.457677  0.252649      0      0  0.161988   
research            0     0.526543  0.280864      0      0  0.126325   

                2020    granada   research  
misinfodem         0          0          0  
covid       0.282616   0.457677   0.526543  
articl      0.208896   0.252649   0.280864  
mechan             0          0          0  
health             0          0          0  
support     0.327071   0.161988   0.126325  
2020               0   0.175514   0.246469  
granada     0.175514          0  0.0742376  
research    0.246469  0.0742376          0  
Pairwise-distance
           misinfodem     covid    articl     mechan     health support  \
misinfodem          0    0.1894  0.293937   0.419015   0.419015       0   
covid          0.1894         0  0.156809   0.234496   0.234496       0   
articl       0.293937  0.156809         0   0.170443   0.170443       0   
mechan       0.419015  0.234496  0.170443          0          0       0   
health       0.419015  0.234496  0.170443          0          0       0   
support             0         0         0          0          0       0   
2020         0.468991  0.293937    0.1894  0.0784044  0.0784044       0   
granada             0         0         0          0          0       0   
research     0.529541  0.350635  0.252533   0.120233   0.120233       0   

                 2020 granada   research  
misinfodem   0.468991       0   0.529541  
covid        0.293937       0   0.350635  
articl         0.1894       0   0.252533  
mechan      0.0784044       0   0.120233  
health      0.0784044       0   0.120233  
support             0       0          0  
2020                0       0  0.0631332  
granada             0       0          0  
research    0.0631332       0          0  

(-0.1562215918892801, 0.701, -0.6326413856143783)

#mantel test for 20 word network
mantel_test(10)

Pairwise-distance
           misinfodem        covid    articl mechan health commun strategi  \
misinfodem          0            0         0      0      0      0        0   
covid               0  7.45058e-09  0.360337      0      0      0        0   
articl              0     0.360337         0      0      0      0        0   
mechan              0            0         0      0      0      0        0   
health              0            0         0      0      0      0        0   
commun              0            0         0      0      0      0        0   
strategi            0            0         0      0      0      0        0   
psycholog           0            0         0      0      0      0        0   
misinform           0            0         0      0      0      0        0   
public              0     0.445426  0.109024      0      0      0        0   
support             0     0.608033  0.404283      0      0      0        0   
2020                0     0.282616  0.208896      0      0      0        0   
granada             0     0.457677  0.252649      0      0      0        0   
research            0     0.526543  0.280864      0      0      0        0   
programm            0     0.526543  0.280864      0      0      0        0   
tabik               0     0.421418  0.189487      0      0      0        0   
garcía              0     0.318922  0.129222      0      0      0        0   
project             0     0.492938  0.220392      0      0      0        0   
decsai              0     0.492938  0.220392      0      0      0        0   

           psycholog misinform    public   support       2020    granada  \
misinfodem         0         0         0         0          0          0   
covid              0         0  0.445426  0.608033   0.282616   0.457677   
articl             0         0  0.109024  0.404283   0.208896   0.252649   
mechan             0         0         0         0          0          0   
health             0         0         0         0          0          0   
commun             0         0         0         0          0          0   
strategi           0         0         0         0          0          0   
psycholog          0         0         0         0          0          0   
misinform          0         0         0         0          0          0   
public             0         0         0  0.315811   0.226376   0.185095   
support            0         0  0.315811         0   0.327071   0.161988   
2020               0         0  0.226376  0.327071          0   0.175514   
granada            0         0  0.185095  0.161988   0.175514          0   
research           0         0  0.189487  0.126325   0.246469  0.0742376   
programm           0         0  0.189487  0.126325   0.246469  0.0742376   
tabik              0         0  0.129222  0.220392   0.148475  0.0631623   
garcía             0         0   0.14582  0.323975  0.0843519   0.161988   
project            0         0  0.126325  0.189487   0.222713  0.0843519   
decsai             0         0  0.126325  0.189487   0.222713  0.0843519   

             research   programm      tabik     garcía    project     decsai  
misinfodem          0          0          0          0          0          0  
covid        0.526543   0.526543   0.421418   0.318922   0.492938   0.492938  
articl       0.280864   0.280864   0.189487   0.129222   0.220392   0.220392  
mechan              0          0          0          0          0          0  
health              0          0          0          0          0          0  
commun              0          0          0          0          0          0  
strategi            0          0          0          0          0          0  
psycholog           0          0          0          0          0          0  
misinform           0          0          0          0          0          0  
public       0.189487   0.189487   0.129222    0.14582   0.126325   0.126325  
support      0.126325   0.126325   0.220392   0.323975   0.189487   0.189487  
2020         0.246469   0.246469   0.148475  0.0843519   0.222713   0.222713  
granada     0.0742376  0.0742376  0.0631623   0.161988  0.0843519  0.0843519  
research            0          0   0.109024   0.218047  0.0631623  0.0631623  
programm            0          0   0.109024   0.218047  0.0631623  0.0631623  
tabik        0.109024   0.109024          0   0.109024  0.0742376  0.0742376  
garcía       0.218047   0.218047   0.109024          0   0.175514   0.175514  
project     0.0631623  0.0631623  0.0742376   0.175514          0          0  
decsai      0.0631623  0.0631623  0.0742376   0.175514          0          0  
Pairwise-distance
           misinfodem     covid     articl       mechan       health  \
misinfodem          0    0.1894   0.293937     0.419015     0.419015   
covid          0.1894         0   0.156809     0.234496     0.234496   
articl       0.293937  0.156809          0     0.170443     0.170443   
mechan       0.419015  0.234496   0.170443            0            0   
health       0.419015  0.234496   0.170443            0            0   
commun       0.419015  0.234496   0.170443  2.63418e-09  1.86265e-09   
strategi     0.360699  0.235213  0.0784044     0.185806     0.185806   
psycholog    0.360699  0.235213  0.0784044     0.185806     0.185806   
misinform    0.505066  0.315666   0.284614     0.115951     0.115951   
public       0.409223  0.240466   0.126266    0.0762243    0.0762243   
support             0         0          0            0            0   
2020         0.468991  0.293937     0.1894    0.0784044    0.0784044   
granada             0         0          0            0            0   
research     0.529541  0.350635   0.252533     0.120233     0.120233   
programm            0         0          0            0            0   
tabik               0         0          0            0            0   
garcía              0         0          0            0            0   
project             0         0          0            0            0   
decsai              0         0          0            0            0   

                 commun   strategi    psycholog misinform     public support  \
misinfodem     0.419015   0.360699     0.360699  0.505066   0.409223       0   
covid          0.234496   0.235213     0.235213  0.315666   0.240466       0   
articl         0.170443  0.0784044    0.0784044  0.284614   0.126266       0   
mechan      2.63418e-09   0.185806     0.185806  0.115951  0.0762243       0   
health      1.86265e-09   0.185806     0.185806  0.115951  0.0762243       0   
commun                0   0.185806     0.185806  0.115951  0.0762243       0   
strategi       0.185806          0            0  0.300211   0.115951       0   
psycholog      0.185806          0  3.72529e-09  0.300211   0.115951       0   
misinform      0.115951   0.300211     0.300211         0   0.185806       0   
public        0.0762243   0.115951     0.115951  0.185806          0       0   
support               0          0            0         0          0       0   
2020          0.0784044   0.170443     0.170443  0.152449  0.0631332       0   
granada               0          0            0         0          0       0   
research       0.120233   0.229409     0.229409   0.14117   0.126266       0   
programm              0          0            0         0          0       0   
tabik                 0          0            0         0          0       0   
garcía                0          0            0         0          0       0   
project               0          0            0         0          0       0   
decsai                0          0            0         0          0       0   

                 2020 granada   research programm tabik garcía project decsai  
misinfodem   0.468991       0   0.529541        0     0      0       0      0  
covid        0.293937       0   0.350635        0     0      0       0      0  
articl         0.1894       0   0.252533        0     0      0       0      0  
mechan      0.0784044       0   0.120233        0     0      0       0      0  
health      0.0784044       0   0.120233        0     0      0       0      0  
commun      0.0784044       0   0.120233        0     0      0       0      0  
strategi     0.170443       0   0.229409        0     0      0       0      0  
psycholog    0.170443       0   0.229409        0     0      0       0      0  
misinform    0.152449       0    0.14117        0     0      0       0      0  
public      0.0631332       0   0.126266        0     0      0       0      0  
support             0       0          0        0     0      0       0      0  
2020                0       0  0.0631332        0     0      0       0      0  
granada             0       0          0        0     0      0       0      0  
research    0.0631332       0          0        0     0      0       0      0  
programm            0       0          0        0     0      0       0      0  
tabik               0       0          0        0     0      0       0      0  
garcía              0       0          0        0     0      0       0      0  
project             0       0          0        0     0      0       0      0  
decsai              0       0          0        0     0      0       0      0  

(-0.16261206284337587, 0.8495, -1.04195817588891)

3. Paragraph level

LSA on covid-science.txt at paragraph level

#loading dataset
def load_data(path,file_name):
    """
    Input  : path and file_name
    Purpose: loading text file
    Output : list of paragraphs/documents and
             title(initial 100 words considred as title of document)
    """
    documents_list = []
    titles=[]
    with open( os.path.join(path, file_name) ,"r",encoding='UTF8') as fin:
        data = fin.read()
        words = data.split()
        fin.seek(0)
        print('Number of words in text file :', len(words))
        for line in fin.readlines():
            text = line.strip()
            if text !='':
                documents_list.append(text)
    print("Total Number of Documents:",len(documents_list))
    titles.append( text[0:min(len(text),100)] )
    return documents_list,titles,len(words)

document_list,titles,word_count_science=load_data("","covid-science-large.txt")

Number of words in text file : 274941
Total Number of Documents: 31577

#data preprocessing
clean_text=preprocess_data(document_list)

['covidgr', 'dataset', 'covid', 'sdnet']
['methodology', 'predicting', 'covid']
['based', 'chest', 'images']
['tabik', 'gómez', 'ríos', 'martín', 'rodríguez', 'sevillano', 'garcía', 'area', 'charte']
['guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova']
['olmedo', 'sánchez', 'herrera']
['abstract', 'currently', 'coronavirus', 'disease', 'covid']
['most', 'infectious', 'diseases', '21st', 'century', 'diagnosed', 'using', 'testing', 'scans']
['chest', 'images', 'computed', 'tomography']
['scanners', 'testing', 'available', 'most']
['medical', 'centers', 'hence', 'many', 'cases', 'images']
['become', 'most', 'time', 'cost', 'effective', 'tool', 'assisting', 'clinicians', 'making', 'decisions', 'deep', 'learning', 'neural', 'networks']
['have', 'great', 'potential', 'building', 'covid', 'triage', 'systems']
['detecting', 'covid', 'patients', 'especially', 'patients', 'with']
['severity', 'unfortunately', 'current', 'databases', 'allow']
['building', 'such', 'systems', 'they', 'highly', 'heterogeneous']
['biased', 'towards', 'severe', 'cases', 'this', 'article', 'threefold', 'demystify', 'high', 'sensitivities', 'achieved']
['most', 'recent', 'covid', 'classification', 'models', 'under']
['close', 'collaboration', 'with', 'hospital', 'universitario', 'clínico']
['cecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'that', 'includes', 'levels']
['manuscript', 'received', 'september', '2020', 'revised', 'october', '2020']
['accepted', 'november', '2020', 'date', 'publication', 'november', '2020']
['date', 'current', 'version', 'december', '2020', 'this', 'work', 'supported']
['project', 'deepscop', 'ayudas', 'fundación', 'bbva', 'equipos', 'investigación', 'científica', 'data', '2018', 'covid19_rx', 'ayudas', 'fundación']
['bbva', 'equipos', 'investigación', 'científica', 'sars', 'covid']
['2020', 'spanish', 'ministry', 'science', 'technology', 'under']
['project', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 'cajal']
['programme', '2015', '18136', 'gómez', 'ríos', 'supported']
['programme', 'fpu16', '04765', 'charte', 'supported']
['programme', 'fpu17', '04069', 'suárez', 'supported', 'programme', 'fpu18', '05989', 'supported', 'european', 'research']
['council', 'grant', 'agreement', '647038', 'biodesert', 'this', 'project']
['approved', 'provincial', 'research', 'ethics', 'committee', 'granada']
['corresponding', 'author', 'siham', 'tabik']
['tabik', 'gómez', 'ríos', 'sevillano', 'garcía', 'charte', 'suárez']
['luengo', 'herrera', 'with', 'andalusian', 'research', 'institute']
['data', 'science', 'computational', 'intelligence', 'university', 'granada']
['18071', 'granada', 'spain', 'mail', 'siham', 'anabelgrios', 'decsai']
['isega24ivan', 'gmail', 'fdavidcl', 'jlsuarezdiaz']
['julianlm', 'decsai', 'herrera', 'decsai']
['martín', 'rodríguez', 'valero', 'gonzález', 'garcía', 'villanova']
['olmedo', 'sánchez', 'with', 'hospital', 'universitario', 'clínico']
['cecilio', 'granada', '36310', 'spain', 'mail', 'joseluismartin']
['hotmail', 'valerogonzalez', 'yahoo', 'pgvillanova', 'gmail']
['euolm', 'yahoo']
['area', 'with', 'atlanttic', 'research', 'center', 'telecommunication', 'technologies', 'university', 'vigo', 'galicia', 'spain', 'mail']
['mreyarea', 'gmail']
['guirado', 'with', 'multidisciplinary', 'institute', 'environment']
['studies', 'ramón', 'margalef', 'university', 'alicante', '03690', 'spain', 'mail']
['geesecillo', 'gmail']
['digital', 'object', 'identifier', '1109', 'jbhi', '2020', '3037127']
['severity', 'from', 'normal', 'with', 'positive', 'mild', 'moderate', 'severe', 'covidgr', 'contains', 'positive']
['negative', 'posteroanterior', 'views']
['propose', 'covid', 'smart', 'data', 'based', 'network', 'covid', 'sdnet']
['methodology', 'improving', 'generalization', 'capacity']
['covid', 'classification', 'models', 'approach', 'reaches', 'good']
['stable', 'results', 'with', 'accuracy']
['severe', 'moderate']
['mild', 'covid', 'severity', 'levels', 'approach', 'could', 'help']
['early', 'detection', 'covid', 'covidgr', 'along', 'with']
['severity', 'level', 'labels', 'available', 'scientific', 'community', 'through', 'this', 'link', 'https', 'dasci', 'transferencia']
['open', 'data', 'covidgr']
['index', 'terms', 'covid', 'convolutional', 'neural', 'networks']
['smart', 'data']
['introduction']
[]
['last', 'months', 'world', 'been', 'witnessing']
['covid', 'pandemic', 'increasingly', 'infecting', 'large', 'mass']
['people', 'very', 'fast', 'everywhere', 'world', 'trends']
['clear', 'some', 'research', 'confirm', 'that', 'this', 'problem']
['persist', 'until', '2024', 'besides', 'prevalence', 'studies', 'conducted']
['several', 'countries', 'reveal', 'that', 'tiny', 'proportion', 'population']
['have', 'developed', 'antibodies', 'after', 'exposure', 'virus']
['spain', 'this', 'means', 'that', 'frequently', 'large', 'number', 'patients']
['will', 'need', 'assessed', 'small', 'time', 'intervals', 'number']
['clinicians', 'with', 'very', 'resources']
['general', 'covid', 'diagnosis', 'carried', 'using', 'least']
['these', 'three', 'tests', 'computed', 'tomography', 'scans', 'based', 'assessment']
['consists', 'analyzing', 'radiographic', 'images', 'from', 'different', 'angles', 'needed', 'equipment', 'this', 'assessment']
['available', 'most', 'hospitals', 'takes', 'more', 'than']
['minutes', 'patient', 'addition', 'time', 'required']
['decontamination']
['reverse', 'transcription', 'polymerase', 'chain', 'reaction', 'rtpcr', 'test', 'detects', 'viral', 'from', 'sputum']
['online', 'available', 'https', 'english', 'elpais', 'society', '2020']
['antibody', 'study', 'shows', 'just', 'spaniards', 'have', 'contracted', 'coronavirus']
['html', 'online', 'available', 'advocacy', 'economics', 'acrposition', 'statements', 'recommendations', 'chest', 'radiography', 'forsuspected', 'covid19', 'infection']
['ieee', '2020', 'this', 'article', 'free', 'access', 'download', 'along', 'with', 'rights', 'full', 'text', 'data', 'mining', 'analysis']
['authorized', 'licensed', 'limited', 'university', 'hertfordshire', 'downloaded', 'july', '2021', 'from', 'ieee', 'xplore', 'restrictions', 'apply']
['3596', 'ieee', 'journal', 'biomedical', 'health', 'informatics', 'december', '2020']
['stratification', 'radiological', 'severity', 'covid', 'examples', 'rale', 'index', 'calculated']
['nasopharyngeal', 'swab', 'requires', 'specific', 'material']
['equipment', 'which', 'easily', 'accessible', 'takes']
['least', 'hours', 'which', 'desirable', 'positive', 'covid19', 'patients', 'should', 'identified', 'tracked', 'soon', 'possible', 'some', 'studies', 'found', 'that', 'results', 'from', 'several']
['tests', 'different', 'points', 'from', 'same', 'patients', 'were', 'variable', 'during', 'course', 'illness', 'producing', 'high', 'falsenegative', 'rate', 'authors', 'suggested', 'that', 'test']
['should', 'combined', 'with', 'other', 'clinical', 'tests', 'such']
['chest', 'required', 'equipment', 'this']
['assessment', 'less', 'cumbersome', 'lightweight']
['transportable', 'general', 'this', 'type', 'resources', 'more']
['available', 'than', 'required', 'scan', 'tests']
['addition', 'test', 'takes', 'about', 'seconds', 'patient']
['which', 'makes', 'most', 'time', 'cost', 'effective']
['assessment', 'tools']
['recent', 'studies', 'provide', 'estimates', 'expert', 'radiologists']
['sensitivity', 'diagnosis', 'covid', 'based', 'scans']
['study', 'patients', 'with', 'chest']
['essay', 'performed', 'within', 'days', 'reported']
['sensitivity', 'compared', 'with', 'sensitivity']
['different', 'study', 'patients', 'mean']
['years', 'reported', 'sensitivity', 'compared']
['with', 'initial', 'according', 'analysis']
['ambulatory', 'patients', 'most', 'patients', 'presenting', 'urgent', 'care']
['centers', 'with', 'confirmed', 'coronavirus', 'disease', '2019', 'have', 'normal']
['mildly', 'abnormal', 'findings', 'only', 'these', 'patients']
['correctly', 'diagnosed', 'expert']
['recent', 'study', 'authors', 'proposed', 'simplifying', 'quantification', 'level', 'severity', 'adapting', 'previously', 'defined']
['radiographic', 'assessment', 'lung', 'edema', 'rale', 'score']
['covid', 'this', 'score', 'calculated', 'assigning', 'value']
['between', 'each', 'lung', 'depending', 'extent', 'visual']
['features', 'such', 'consolidation', 'ground', 'glass', 'opacities']
['four', 'parts', 'each', 'lung', 'depicted', 'based', 'this', 'score']
['experts', 'identify', 'level', 'severity', 'infection', 'among']
['four', 'severity', 'stages', 'normal', 'mild', 'moderate']
['severe', 'practice', 'patient', 'classified', 'expert', 'radiologist']
['authorized', 'licensed', 'limited', 'university', 'hertfordshire', 'downloaded', 'july', '2021', 'from', 'ieee', 'xplore', 'restrictions', 'apply']
['tabik', 'covidgr', 'dataset', 'covid', 'sdnet', 'methodology', 'predicting', 'covid', 'based', 'chest', 'images', '3597']
['normal', 'have', 'positive', 'refer', 'these', 'cases']
['normal', 'expert', 'annotation', 'adopted', 'this', 'work', 'based']
['this', 'score']
['automated', 'image', 'analysis', 'deep', 'learning', 'models']
['have', 'great', 'potential', 'optimize', 'role', 'images']
['fast', 'diagnosis', 'covid', 'robust', 'accurate']
['model', 'could', 'serve', 'triage', 'method', 'support']
['medical', 'decision', 'making', 'increasing', 'number', 'recent', 'works']
['claim', 'achieving', 'impressive', 'sensitivities', 'higher', 'than']
['expert', 'radiologists', 'these', 'high', 'sensitivities', 'bias']
['most', 'used', 'covid', 'dataset', 'covid', 'image', 'data']
['collection', 'this', 'dataset', 'includes', 'very', 'small', 'number']
['covid', 'positive', 'cases', 'coming', 'from', 'highly', 'heterogeneous']
['sources', 'least', 'countries', 'most', 'cases', 'severe', 'patients']
['issue', 'that', 'drastically', 'reduces', 'clinical', 'value', 'populate']
['covid', 'healthy', 'classes', 'researchers', 'using']
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['generations', 'have', 'very', 'high', 'covid', 'vaccination', 'rates']
['order', 'guarantee', 'herd', 'immunity', 'there', 'already']
['significant', 'opposition', 'existing', 'vaccinations', 'against']
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['associate', 'editor', 'coordinating', 'review', 'this', 'manuscript']
['approving', 'publication', 'derek', 'abbott']
['cases', '2019', 'measles', 'outbreak']
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['health', 'practitioners', 'governments']
['online', 'social', 'media', 'platforms', 'particular', 'built', 'communities', 'that', 'platforms', 'like', 'facebook', 'feature']
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['distrust', 'government', 'pharmaceutical', 'industry']
['technologies', 'such', 'communications', 'adding']
['fuel', 'this', 'fire', 'january', '2020', 'birth', 'covid']
['infodemic', 'plethora', 'misinformation']
['social', 'media', 'surrounding', 'covid', 'that', 'directly', 'threatens']
['lives', 'example', 'harmful', 'cures', 'being', 'proposed']
['such', 'drinking', 'fish', 'tank', 'additives', 'bleach', 'urine']
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['population', 'addition', 'world']
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[]
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[]
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['covid', 'known', 'infectious', 'disease', 'caused']
['severe', 'acute', 'respiratory', 'syndrome', 'sars']
['named', 'coronavirus', 'visual', 'appearance', 'under']
['electron', 'microscope', 'solar', 'corona', 'similar', 'crown']
['fight', 'against', 'covid', 'motivated', 'researchers']
['worldwide', 'explore', 'understand', 'devise', 'diagnostic']
['treatment', 'techniques', 'culminate', 'this', 'threat', 'eration', 'this', 'article', 'discuss', 'computer', 'vision']
['community', 'fighting', 'with', 'this', 'menace', 'proposing']
['types', 'approaches', 'improving', 'efficiency', 'speed']
['existing', 'efforts']
['scientific', 'response', 'combat', 'covid', 'been']
['quicker', 'widespread', 'keyword', 'search', 'pubmed']
['major', 'open', 'access', 'preprint', 'repositories', 'arxiv']
['biorxiv', 'medrxiv', 'revealed', 'that', '2019', 'lished', 'papers', 'included', 'word', 'coronavirus', 'figure']
['illustrates', 'findings', 'during', 'first', 'half', '2020', 'this']
['number', 'increased', 'thirty', 'fold', 'rose', 'astounding']
['associate', 'editor', 'coordinating', 'review', 'this', 'manuscript']
['approving', 'publication', 'derek', 'abbott']
['articles', 'comparison', 'sars', 'pandemic', 'with']
['less', 'than', 'confirmed', 'infections', 'deaths']
['roughly', 'four', 'fold', 'increase', 'over', 'years', '2002']
['2004', 'after', 'occurrence', 'mers', '2012']
['less', 'than', 'confirmed', 'infections', 'deaths']
['date', 'doubling', 'coronavirus', 'related', 'papers', 'over', 'four', 'years']
['2011', '2015', 'observed']
['economist', 'dubbed', 'current', 'herculean', 'task']
['science', 'times', 'with', 'hope', 'that', 'such', 'efforts', 'would']
['help', 'speed', 'development', 'covid', 'vaccine']
['numerous', 'approaches', 'computer', 'vision', 'have', 'been', 'posed', 'dealing', 'with', 'different', 'aspects', 'combat']
['covid', 'pandemic', 'these', 'approaches', 'vary', 'terms']
['their', 'approach', 'fundamental', 'questions']
['medical', 'imaging', 'facilitate', 'faster', 'reliable']
['diagnosis', 'covid']
['which', 'image', 'features', 'correctly', 'classify', 'conditions']
['bacterial', 'viral', 'covid', 'pneumonia']
['what', 'learn', 'from', 'imaging', 'data', 'acquired', 'from']
['disease', 'survivors', 'screen', 'critical', 'critical']
['patients']
['volume', '2020', 'this', 'work', 'licensed', 'under', 'creative', 'commons', 'attribution', 'license', 'more', 'information', 'https', 'creativecommons', 'licenses', '179437a', 'ulhaq', 'covid', 'control', 'computer', 'vision', 'approaches', 'survey']
['figure', 'portrayal', 'current', 'increase', 'research', 'articles', 'about', 'coronavirus', 'related', 'research', 'since', 'their', 'discovery', 'early', '1960s']
['coronavirus', 'research', 'increased', 'substantially', 'especially', 'after', 'sars', 'outbreak', '2002', 'made', 'clear', 'their', 'pandemic', 'potential', 'previously']
['most', 'productive', 'full', 'year', '2004', 'with', 'coronavirus', 'papers', 'sars', 'pandemic', 'caused', 'leap', 'with', 'articles', 'only']
['first', 'half', '2020', 'reference', 'date', 'analysis', 'june', '2020', 'note', 'that', 'axis', 'displayed', 'scale', 'visual', 'clarity', 'that']
['height', 'coloured', 'bars', 'shows', 'their', 'relative', 'contribution']
['computer', 'vision', 'used', 'enforce', 'social']
['distancing', 'early', 'screening', 'infected', 'people']
['computer', 'vision', 'help', 'maintain', 'health', 'care', 'equipment', 'supply', 'guide', 'development']
['covid', 'vaccine']
['answers', 'these', 'questions', 'being', 'explored']
['preliminary', 'work', 'been', 'done']
['contribution', 'this', 'review', 'article', 'follows']
['this', 'review', 'article', 'classifies', 'covid', 'related', 'computer', 'vision']
['methods', 'into', 'broad', 'categories', 'provides', 'salient', 'descrip', 'tions', 'representative', 'methods', 'each', 'group', 'aspire']
['give', 'readers', 'ability', 'understand', 'baseline', 'efforts']
['kickstart', 'their', 'work', 'where', 'others', 'have', 'left', 'furthermore']
['highlight', 'trends', 'innovative', 'ideas', 'build']
['more', 'robust', 'well', 'planned', 'strategy', 'during', 'this']
['times']
['survey', 'will', 'also', 'include', 'research', 'articles', 'print']
['format', 'time', 'urgency', 'imposed', 'this', 'disease']
['however', 'limitation', 'this', 'review', 'inclusion']
['risk', 'lower', 'quality', 'work', 'without', 'validation']
['many', 'works', 'have', 'been', 'into', 'clinical', 'trial']
['time', 'consuming', 'nevertheless', 'intention', 'here']
['share', 'ideas', 'from', 'single', 'platform', 'while', 'highlighting']
['computer', 'vision', 'community', 'efforts', 'hope', 'that', 'reader']
['aware', 'these', 'contemporary', 'challenges', 'this', 'article']
['extended', 'revised', 'version', 'earlier', 'preprint', 'survey']
['follow', 'down', 'approach', 'describe', 'research']
['problems', 'that', 'require', 'urgent', 'attention', 'start', 'with', 'disease']
['diagnosis', 'discuss', 'disease', 'prevention', 'control', 'followed']
['treatment', 'related', 'computer', 'vision', 'research', 'work']
['have', 'organised', 'paper', 'follow', 'section', 'describes']
['overall', 'taxonomy', 'computer', 'vision', 'research', 'areas']
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['section', 'describes', 'available', 'resources', 'including', 'research']
['datasets', 'their', 'links', 'deep', 'learning', 'models', 'codes']
['section', 'provides', 'discussion', 'future', 'work', 'directions']
['followed', 'concluding', 'remarks', 'references']
['historical', 'development']
['novel', 'coronavirus', 'sars', 'seventh', 'member']
['corona', 'viridae', 'family', 'viruses', 'which', 'enveloped']
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['mers', 'however', 'highly', 'infectious']
['number', 'cases', 'continuous', 'rise']
['disease', 'outbreak', 'first', 'reported', 'wuhan', 'hubei']
['province', 'china', 'after', 'several', 'cases', 'pneumonia', 'with']
['unknown', 'causes', 'were', 'reported', 'december', '2019']
['novel', 'coronavirus', 'discovered', 'causative']
['organism', 'through', 'depth', 'sequencing', 'analysis', 'ples', 'patient', 'respiratory', 'tract', 'chinese', 'facilities']
['january', '2020', 'outbreak', 'announced']
['public', 'health', 'emergency', 'international', 'concern']
['january', '2020', 'february', '2020', 'world', 'health']
['organization', 'announced', 'name', 'coron', 'avirus', 'disease', 'covid', 'officially', 'being', 'considered']
['pandemic', 'after', 'march', 'announcement']
['taxonomy', 'areas', 'research']
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['covid', 'better', 'comprehensibility', 'have', 'classified']
['them', 'into', 'three', 'areas', 'research', 'diagnosis', 'prog', 'nosis', 'disease', 'prevention', 'control', 'disease']
['treatment', 'management', 'figure', 'shows', 'this', 'taxonomy']
['following', 'subsections', 'discuss', 'research', 'fields']
['179438', 'volume', '2020a', 'ulhaq', 'covid', 'control', 'computer', 'vision', 'approaches', 'survey']
['figure', 'classification', 'computer', 'vision', 'approaches', 'covid', 'control', 'survey', 'classifies', 'covid', 'related', 'computer', 'vision']
['methods', 'into', 'three', 'broad', 'categories']
['relevant', 'papers', 'present', 'brief', 'representative', 'descrip', 'tion', 'related', 'works']
['diagnosis', 'prognosis']
['essential', 'step', 'this', 'fight', 'reliable', 'faster', 'afford', 'able', 'diagnostic', 'process', 'that', 'readily', 'accessible']
['available', 'global', 'community', 'according', 'cambridge']
['dictionary', 'diagnosis', 'making', 'judgment', 'about']
['exact', 'character', 'disease', 'other', 'problem', 'especially']
['after', 'examination', 'such', 'judgment', 'prognosis']
['doctor', 'judgment', 'likely', 'expected', 'development']
['disease', 'chances', 'getting', 'better']
['currently', 'reverse', 'transcriptase', 'quantitative', 'polymerase']
['chain', 'reaction', 'qpcr', 'tests', 'considered', 'gold']
['standard', 'diagnosing', 'covid', 'during', 'such', 'test']
['small', 'amounts', 'viral', 'extracted', 'from', 'nasal']
['swab', 'amplified', 'quantified', 'virus', 'detection', 'then', 'formed', 'using', 'fluorescent', 'although', 'accurate', 'test']
['time', 'consuming', 'manual', 'requires', 'biomolecular', 'test', 'facilities', 'which', 'limits', 'availability', 'large', 'scales']
['third', 'world', 'countries', 'care', 'taken', 'interpreting']
['negative', 'test', 'results', 'meta', 'study', 'estimated', 'sensitivity']
['over', 'disease', 'process', 'found', 'maximal', 'sensitivity']
['eight', 'days', 'after', 'infection', 'some', 'studies', 'have']
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['computed', 'tomography', 'scan']
['alternative', 'approach', 'radiology', 'examination']
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['scan', 'invasive', 'test', 'conducted', 'obtain', 'precise']
['image', 'patient', 'chest', 'uses', 'enhanced', 'form']
['technology', 'providing', 'more', 'detailed', 'images', 'chest', 'than']
['standard', 'produces', 'images', 'that', 'include', 'bones', 'fats']
['muscles', 'organs', 'giving', 'physicians', 'better', 'view', 'which']
['crucial', 'when', 'making', 'accurate', 'diagnoses']
['chest', 'scan', 'types', 'namely', 'high', 'resolution']
['spiral', 'chest', 'scan', 'high', 'resolution', 'chest']
['scan', 'provides', 'more', 'than', 'slice', 'image', 'single', 'rotation']
['tube', 'spiral', 'chest', 'scan', 'application']
['involves', 'table', 'that', 'continuously', 'moves', 'through', 'tunnel', 'like']
['hole', 'while', 'tube', 'follows', 'spiral', 'path', 'advan', 'tage', 'spiral', 'that', 'capable', 'producing']
['three', 'dimensional', 'image', 'lungs']
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['covid', 'pneumonia', 'include', 'bilateral', 'pleural', 'areas']
['ground', 'glass', 'opacification', 'consolidation', 'affecting']
['lower', 'lobes', 'within', 'intermediate', 'stage', 'days', 'from']
['symptom', 'onset', 'crazy', 'paving', 'pattern', 'possibly', 'observ', 'able', 'halo', 'sign', 'become', 'important', 'features', 'well']
['case', 'images', 'shown']
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['sensitivity', 'positive', 'predictive', 'value', 'negative', 'predic', 'tive', 'value', 'area', 'under', 'curve', 'score']
['quick', 'elucidation', 'their', 'definition', 'useful']
['accuracy', 'method', 'finds', 'correct', 'values', 'dicted', 'precision', 'finds', 'reproducibility', 'surement', 'recall', 'presents', 'many', 'correct', 'results']
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[]
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['range', 'with', 'scale']
['multilayer', 'perceptron', 'classification', 'posed', 'covid', 'recognition', 'pipeline', 'modified']
['replacing', 'csen', 'part', 'with', 'another', 'classifier']
['most', 'common', 'classifiers', 'hidden', 'layer', 'multilayer']
['perceptron', 'used', 'this', 'problem', 'shown']
['training', 'used', 'back', 'propagation', 'with', 'adam']
['optimization', 'technique', 'network', 'training', 'hyper', 'parameters', 'follows', 'learning', 'rate']
['moment', 'updates', 'number']
['epochs', 'illustrates', 'network', 'configuration', 'detail']
['this', 'network', 'configuration', 'achieved', 'best', 'performance']
['among', 'others', 'deeper', 'shallower', 'where', 'deep', 'configura', 'tions', 'have', 'suffered', 'from', 'over', 'fitting', 'while', 'shallow', 'ones']
['exhibit', 'inferior', 'learning', 'performance']
['support', 'vector', 'machines', 'svms', 'multiclass']
['problem', 'first', 'objective', 'select', 'topology']
['ensemble', 'learning', 'versus', 'versus', 'order']
['find', 'optimal', 'topology', 'hyperparameters', 'type', 'parameters', 'first', 'performed', 'grid', 'search']
['with', 'following', 'variations', 'setting', 'kernel', 'function1816', 'ieee', 'transactions', 'neural', 'networks', 'learning', 'systems', '2021']
['configuration']
['table']
['classification', 'performances', 'proposed', 'csen']
['competing', 'methods', 'best', 'covid', 'recognition']
['rates', 'highlighted']
['linear', 'radial', 'basis', 'function', 'constraint']
['parameter', 'range', 'with', 'scale']
['kernel', 'scale', 'kernel', 'range']
['with', 'scale']
['nearest', 'neighbor', 'finally', 'traditional']
['approach', 'nearest', 'neighbor', 'used', 'with', 'dimen', 'sionality', 'reduction', 'similar', 'fashion', 'distance', 'metric']
['value', 'optimized', 'prior', 'grid', 'search']
['following', 'distance', 'metrics', 'evaluated', 'city', 'block', 'cheby', 'shev', 'correlation', 'cosine', 'euclidean', 'hamming', 'jaccard', 'maha', 'lanobis', 'minkowski', 'standardized', 'euclidean', 'spearman']
['metrics', 'value', 'varied', 'within', 'range', '4416']
['with', 'scale']
['experimental', 'results']
['experimental', 'setup']
['have', 'performed', 'experiments', 'over']
['qata', 'cov19', 'data', 'which', 'consists', 'normal']
['three', 'pneumonia', 'classes', 'bacterial', 'viral', 'covid']
['table']
['number', 'images', 'class', 'fold', 'before']
['after', 'data', 'augmentation']
['proposed', 'approach', 'evaluated', 'using', 'stratified', 'fivefold']
['cross', 'validation', 'scheme', 'with', 'ratio', 'training']
['test', 'unseen', 'folds', 'splits', 'respectively']
['table', 'shows', 'number', 'images', 'class']
['qata', 'cov19', 'data', 'since', 'data', 'unbalanced', 'have']
['applied', 'data', 'augmentation', 'training', 'order', 'ance', 'size', 'each', 'class', 'train', 'therefore']
['images', 'viral', 'covid', 'pneumonia', 'normal', 'classes']
['augmented', 'same', 'number', 'bacterial', 'pneu', 'monia', 'class', 'train', 'image', 'data', 'generator']
['keras', 'perform', 'data', 'augmentation', 'randomly', 'rotating']
['images', 'range', 'randomly', 'shifting', 'images', 'both']
['horizontally', 'vertically', 'within', 'interval']
['each', 'fold', 'total', '8832', '1257', 'images']
['train', 'test', 'unseen', 'fold', 'sets', 'respectively']
['experimental', 'evaluations']
['performed', 'using', 'matlab', 'version', '2019a', 'running', 'with']
['intel', '8650u', 'system', 'memory']
['other', 'hand', 'csen', 'methods', 'implemented', 'using']
['tensorflow', 'library', 'with', 'python', 'nvidia', 'titan']
['card', 'csen', 'training', 'adam', 'optimizer']
['used', 'with', 'proposed', 'default', 'learning', 'parameters', 'learning']
['rate', 'moment', 'updates']
['with', 'only', 'back', 'propagation', 'epochs', 'neither', 'grid', 'search']
['other', 'parameter', 'configuration', 'optimization']
['performed', 'csen']
['experimental', 'results']
['same', 'network', 'configurations', 'used', 'csen']
['accordingly', 'compact', 'csen', 'designs']
['csen1', 'csen2', 'respectively', 'first', 'csen', 'network']
['consists', 'only', 'hidden', 'convolutional', 'layers', 'first']
['layer', 'neurons', 'second', 'relu', 'activation']
['function', 'used', 'hidden', 'layers', 'filter', 'size']
['other', 'hand', 'csen2', 'uses', 'pooling']
['additional', 'hidden', 'layer', 'with', 'neurons', 'perform', 'transposed', 'convolution', 'csen1', 'csen2', 'compared', 'against']
['competing', 'methods', 'under', 'same', 'experimental', 'setup']
['dictionary', 'construction', 'each', 'csen', 'design']
['images', 'each', 'class', 'from', 'augmented', 'training', 'samples']
['fold', 'stacked', 'such', 'that', 'representation', 'coeffi', 'cient', 'plane', 'size', 'shown']
['rest', 'images', 'training', 'used', 'train']
['each', 'csen', '1583', 'samples', 'from', 'each', 'class']
['dimensional', 'reduction', 'matrix', 'with', 'compression', 'ratio']
['therefore', 'have', '2500', 'equivalentyamaç', 'convolutional', 'sparse', 'support', 'estimator', 'based', 'covid', 'recognition', '1817']
['table']
['number', 'network', 'parameters', 'each', 'method']
['dictionary', '2500', 'denoiser']
[]
[]
[]
['obtain', 'coarse', 'estimation', 'representation', 'sparse']
['ideal', 'case', 'coefficients', '2500', 'hereafter', 'csen']
['networks', 'trained', 'obtain', 'class', 'information', 'from']
['input', 'illustrated']
['lack', 'other', 'learning', 'based', 'studies']
['literature', 'chose', 'deeper', 'network', 'compared', 'csen']
['designs', 'investigate', 'role', 'network', 'depth', 'this']
['problem', 'reconnet', 'proposed', 'noniterative', 'deep']
['learning', 'solution', 'problem']
['state', 'compressively', 'sensed', 'image', 'recognition']
['task', 'consists', 'fully', 'convolutional', 'layers', 'dense']
['layer', 'front', 'convolutional', 'ones', 'which']
['learned', 'denoiser', 'mapping', 'from']
['then', 'convolutional', 'layers', 'responsible', 'producing']
['reconstructed', 'signal', 'from', 'therefore', 'replacing', 'this']
['dense', 'layer', 'with', 'denoiser', 'matrix', 'this', 'network']
['used', 'competing', 'method']
['both', 'csen', 'modified', 'reconnet', 'input']
['which', 'produced', 'using', 'equivalent', 'dictionary']
['pseudo', 'inverse', 'matrix']
['designing', 'dictionary', 'system', 'training']
['samples', 'stacked', 'dictionary', '2208', 'samples']
['from', 'each', 'class', 'same', 'matrix', 'used', 'csen', 'based']
['recognition', 'applied', 'features', '1024', 'therefore']
['dictionary', 'size', '8832', 'corresponding']
['denoiser', 'matrix', 'size', '8832', 'used']
['framework']
['overall', 'confusion', 'matrix', 'elements', 'formed', 'lows', 'true', 'positive', 'number', 'correctly', 'detected']
['positive', 'class', 'members', 'true', 'negative', 'number', 'rectly', 'detected', 'negative', 'class', 'samples', 'false', 'positive']
['number', 'misclassified', 'negative', 'class', 'members', 'positive']
['number', 'misclassified', 'positive', 'class', 'samples']
['negative', 'missed', 'positive', 'cases', 'then', 'standard']
['performance', 'evaluation', 'metrics', 'defined', 'follows']
['sensitivity']
[]
['where', 'sensitivity', 'recall', 'rate', 'correctly', 'detected']
['positive', 'samples', 'positive', 'class']
['specificity']
[]
['where', 'specificity', 'ratio', 'accurately', 'detected', 'negative']
['class', 'samples', 'negative', 'class']
['precision']
[]
['where', 'precision', 'rate', 'correctly', 'classified', 'positive', 'class']
['samples', 'among', 'members', 'classified', 'positive', 'sample']
['accuracy']
[]
['table']
['computation', 'times', 'each', 'method', 'over', '1257', 'test', 'images']
['table']
['overall', 'cumulative', 'confusion', 'matrix']
['proposed', 'recognition', 'scheme']
['where', 'accuracy', 'ratio', 'correctly', 'classified', 'elements']
['among', 'data']
[]
['precision', 'sensitivity']
[]
['precision']
['sensitivity']
['where', 'score', 'defined', 'weighting', 'parameter']
['score', 'calculated', 'with', 'which', 'harmonic']
['average', 'precision', 'sensitivity']
['classification', 'performance', 'proposed', 'csen', 'based']
['approach', 'competing', 'methods', 'presented', 'table']
['easily', 'observed', 'from', 'table', 'proposed']
['approaches', 'surpass', 'competing', 'methods', 'covid', 'recognition', 'performance', 'achieving', 'sensitivity']
['over', 'specificity', 'shown', 'table', 'compared']
['reconnet', 'proposed', 'csen', 'designs']
['very', 'compact', 'computationally', 'efficient', 'this', 'evident']
['table', 'where', 'computational', 'complexity', 'measured']
['total', 'computation', 'time', 'over', '1257', 'test', 'images', 'reported']
['finally', 'table', 'presents', 'overall', 'cumulative', 'confusion']
['matrix', 'proposed', 'csen', 'based', 'covid', 'recognition']
['approach', 'over', 'qata', 'cov19', 'data', 'most', 'critical']
['misclassifications', 'false', 'positives', 'misclassified']
['covid', 'images', 'confusion', 'matrix', 'shows', 'that']
['proposed', 'approach', 'misclassified', 'seven', 'covid', 'images']
['misclassifications', 'still', 'viral']
['pneumonia', 'category', 'which', 'expected', 'confusion']
['viral', 'nature', 'covid', 'however', 'other', 'four']
['cases', 'misclassified', 'normal', 'which', 'indeed', 'severe']
['clinical', 'misdiagnosis', 'close', 'look', 'these', 'false', 'negatives']
['reveals', 'fact', 'that', 'they', 'indeed', 'very', 'similar']
['normal', 'images', 'where', 'typical', 'covid', 'patterns', 'hardly']
['visible', 'even', 'expert', 'naked', 'possible', 'that', 'these']
['images', 'come', 'from', 'patients', 'were', 'very', 'early', 'stages']
['covid']
['discussion']
['versus', 'csen']
['when', 'compared', 'against', 'particular', 'csen', 'based']
['classification', 'advantages', 'computational', 'efficiency']
['superior', 'covid', 'recognition', 'performance']
['computational', 'efficiency', 'comes', 'from', 'fact', 'that', 'larger']
['size', 'dictionary', 'matrix', 'size', '8832', 'used1818', 'ieee', 'transactions', 'neural', 'networks', 'learning', 'systems', '2021']
['proposed', 'covid', 'recognition', 'scheme']
['table']
['performance', 'algorithm', 'when', 'dictionary', 'size']
['class', 'that', 'used', 'csen', 'used']
['hence', 'this', 'requires', 'more', 'computations']
['terms', 'matrix', 'vector', 'multiplications', 'furthermore', 'saving']
['trainable', 'parameters', 'light', 'dictionary', 'matrix']
['coefficients', '1280k', 'test', 'device', 'more', 'memory']
['efficient', 'compared', 'saving', 'coefficients', '4521k', 'larger']
['size', 'dictionary', 'used']
['further', 'analysis', 'also', 'tested', 'framework']
['using', 'light', 'dictionary', 'size', '2500', 'used']
['csen', 'based', 'recognition', 'called', 'light']
['seen', 'table', 'performance', 'further']
['reduced', 'there', 'significant', 'improvement', 'concerning']
['computational', 'cost', 'when', 'comes', 'creating', 'deeper']
['convolutional', 'layers', 'instead', 'using', 'csen', 'designs', 'such']
['modified', 'reconnet', 'results', 'presented', 'table']
['shows', 'that', 'compact', 'csen', 'structures', 'indeed', 'preferable']
['achieve', 'superior', 'classification', 'performances', 'compared']
['deeper', 'networks']
['compact', 'versus', 'deep', 'csens']
['representation', 'based', 'classifications', 'known', 'provid', 'satisfactory', 'performance', 'when', 'comes', 'limited', 'size', 'data']
['sets', 'other', 'hand', 'deep', 'artificial', 'usually', 'require']
['large', 'training', 'achieve', 'satisfactory', 'generalization']
['capability']
['representation', 'based', 'dictionary', 'classification', 'scheme']
['when', 'dictionary', 'size', 'getting', 'bigger', 'increase', 'number']
['training', 'samples', 'computational', 'complexity', 'method']
['drastically', 'increases', 'proposed', 'csen', 'alternative']
['approach', 'handle', 'both', 'moderate', 'scarce', 'data', 'sets']
['compact', 'possible', 'structures', 'dictionary', 'based']
['classification']
['since', 'there', 'other', 'learning', 'based', 'method', 'except']
['csen', 'literature', 'chose', 'reconnet', 'possible']
['competing', 'algorithm', 'this', 'problem', 'explained', 'detail']
['section', 'reconnet', 'fully', 'convolution', 'layers']
['ablation', 'study', 'also', 'more', 'hidden', 'layers']
['proposed', 'csen', 'models', 'compare', 'csen3', 'csen4', 'were', 'obtained', 'adding', 'hidden', 'layers']
['csen2', 'respectively', 'after', 'transposed', 'convolutional', 'layer']
['table']
['performance', 'alternative', 'deeper', 'designs', 'compared']
['compact', 'csens']
['table', 'viii']
['number', 'network', 'parameters', 'competing', 'networks']
['additional', 'layers', 'have', 'neurons', 'relu', 'activation', 'functions']
['filter', 'size', 'observe', 'from', 'tables']
['viii', 'proposed', 'compact', 'designs', 'csen1', 'csen2']
['both', 'surpass', 'deeper', 'counterparts', 'both', 'performance']
['required', 'number', 'parameters']
['conclusion']
['commonly', 'used', 'methods', 'covid', 'diagnosis']
['namely', 'have', 'certain', 'limitations', 'draw', 'backs', 'such', 'long', 'processing', 'times', 'unacceptably', 'high']
['misdiagnosis', 'rates', 'these', 'drawbacks', 'also', 'shared', 'most']
['recent', 'works', 'literature', 'based', 'deep', 'learning']
['data', 'scarcity', 'from', 'covid', 'cases', 'although', 'deep']
['learning', 'based', 'recognition', 'techniques', 'dominant', 'puter', 'vision', 'where', 'they', 'achieved', 'state', 'performance']
['their', 'performance', 'degrades', 'fast', 'data', 'scarcity', 'which']
['reality', 'this', 'problem', 'hand', 'this', 'study', 'aims', 'address']
['such', 'limitations', 'proposing', 'robust', 'highly', 'accurate']
['covid', 'recognition', 'approach', 'directly', 'from', 'images']
['proposed', 'approach', 'based', 'csen', 'that', 'seen']
['bridge', 'between', 'deep', 'learning', 'models', 'representation', 'based', 'methods', 'csen', 'uses', 'both', 'dictionary']
['training', 'samples', 'learn', 'direct', 'mapping', 'from', 'query']
['samples', 'sparse', 'support', 'representation', 'coefficients']
['with', 'this', 'unique', 'ability', 'having', 'advantage', 'compact']
['network', 'proposed', 'csen', 'based', 'covid', 'recognition']
['systems', 'surpass', 'competing', 'methods', 'achieve', 'over']
['sensitivity', 'over', 'specificity', 'furthermore', 'they', 'yield']
['most', 'computationally', 'efficient', 'scheme', 'terms', 'speed']
['memory']
['acknowledgment']
['authors', 'would', 'like', 'thank', 'following', 'medical']
['doctor', 'team', 'their', 'generous', 'feedbacks', 'continuousyamaç', 'convolutional', 'sparse', 'support', 'estimator', 'based', 'covid', 'recognition', '1819']
['proof', 'reading', 'khalid', 'hameed', 'reem', 'medical']
['center', 'doha', 'qatar', 'tahir', 'hamid', 'consultant', 'cardiologist']
['hamad', 'medical', 'corporation', 'hospital', 'with', 'weill', 'cornell']
['medicine', 'qatar', 'doha', 'rashid', 'mazhar', 'hamad']
['medical', 'corporation', 'hospital', 'doha', 'qatar']

# LSA Model
number_of_topics=100
words=word_count_science
model_science=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_law =dict(model_science.show_topic(0, topn=words))##dict and encoding matrix values

#These variables are for further experimentation in vertical comparission mantel test
model_science_paragraph=create_gensim_lsa_model(clean_text,number_of_topics,words)

#dictionary of top 5 words
dict(model_science.show_topic(0, topn=5))

{'covid': 0.8256400912768749,
 'imag': 0.24294931530827157,
 'model': 0.190294345224982,
 'use': 0.1841813457165905,
 'detect': 0.14281538766739102}

#word cloud of top 10 words
from wordcloud import WordCloud
text = dict(model_science.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph25.png')

# Cosine similarity matrix of top 5 words

a=pd.DataFrame()

for i in range(number_of_topics):
    words_science =dict(model_science.show_topic(i, topn=words))
    b=pd.DataFrame(words_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_science.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled28.png')

cosine-similarity
              covid         imag        model          use      detect
covid             1  0.000700957 -7.58371e-05 -0.000371984  0.00520228
imag    0.000700957            1  0.000868205 -0.000432478  0.00039015
model  -7.58371e-05  0.000868205            1 -4.46218e-05 -0.00210096
use    -0.000371984 -0.000432478 -4.46218e-05            1  0.00022151
detect   0.00520228   0.00039015  -0.00210096   0.00022151           1

#networkx graph of top 5 words
df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *100  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='green',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig28.png')

LSA on covid-psychology-large.txt at paragraph level

#loading data
document_list,titles,word_count_psy=load_data("","covid-psychology-large.txt")

Number of words in text file : 183592
Total Number of Documents: 18522

#data preprocesing
clean_text=preprocess_data(document_list)

['international', 'sociology']
['2021']
['author', '2020']
['article', 'reuse', 'guidelines']
['sagepub', 'journals', 'permissions']
['1177', '0268580920948807']
['journals', 'sagepub', 'home']
['psychology', 'politics']
['covid', 'misinfodemics']
['people']
['believe', 'misinfodemics']
['sonia', 'mukhtar']
['university', 'management', 'technology', 'lahore', 'pakistan']
['abstract']
['misinfodemics', 'related', 'covid', 'have', 'negatively', 'impacted', 'people', 'lives', 'with', 'adverse']
['health', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicate']
['evidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'risk', 'emerged', 'effects']
['misinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevier']
['about', 'covid', 'related', 'misinfodemics', 'have', 'been', 'considered', 'reviewed', 'this', 'article', 'this']
['review', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects']
['responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'article']
['shows', 'that', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product']
['psychological', 'predisposition', 'which', 'either', 'reject', 'information', 'from', 'experts', 'perceive']
['crisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations']
['psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'have', 'implications']
['development', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health']
['keywords']
['conspiracy', 'theories', 'covid', 'health', 'communication', 'misinformation', 'mistrust', 'science']
['mechanisms', 'misinfodemics']
['this', 'modern', 'mainstream', 'social', 'media', 'have', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'thus', 'risk', 'misinfodemics', 'surrounding']
['corresponding', 'author']
['sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'town', 'lahore']
['punjab', '54770', 'pakistan']
['email', 'sonia', 'mukhtar12', 'gmail']
['948807iss0010', '1177', '0268580920948807international', 'sociologymukhtar']
['research', 'article2020']
['article']
['international', 'sociology']
['covid', 'pandemic', 'even', 'more', 'challenging', 'curb', 'ongoing', 'coronavirus']
['covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modern']
['globalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origin']
['unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'during']
['lockdown', 'quarantine', 'self', 'isolation', 'social', 'distancing', 'virtual', 'communication']
['become', 'major', 'source', 'interaction', 'holman', '2020']
['covid', 'pandemic', 'outbreak', 'only', 'escalated', 'challenges']
['healthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'over', 'world', 'also', 'gained', 'momentum', 'innumerable']
['misinfodemics', 'mechanisms', 'rumors', 'myths', 'superstitions', 'conspiracy', 'theories']
['claims', 'hoaxes', 'false', 'misinformation', 'fake', 'news', 'polarization', 'mistrust', 'science']
['times', 'crisis', 'absence', 'fact', 'checking', 'misinformation', 'misleading', 'content', 'false']
['context', 'manipulated', 'data', 'fabricated', 'material', 'imposter', 'documents', 'disinformation', 'regarding', 'etiology', 'outcomes', 'misplaced', 'prevention', 'disease']
['mukhtar', '2020a', '2020']
['covid', 'pandemic', 'outbreak', 'saturated', 'mainstream', 'media', 'which', 'disseminate', 'information', 'local', 'global', 'scale', 'similarly', 'social', 'media', 'platforms', 'have']
['also', 'become', 'accessible', 'source', 'information', 'numerous', 'incidents', 'initiated']
['these', 'rumors', 'have', 'caused', 'several', 'mishaps', 'across', 'world', 'instance', 'took']
['life', 'after', 'positive', 'diagnosis', 'covid', 'india', 'people', 'have', 'overdosed']
['drug', 'cholorquine', 'after', 'news', 'about', 'effectiveness', 'against', 'covid', 'proliferated', 'nigeria', 'busari', 'adebayo', '2020', '2020', 'there', 'been', 'failure', 'make']
['distinction', 'between', 'empirically', 'based', 'scientific', 'truths', 'fabricated', 'unconfirmed']
['anti', 'science', 'conspiracy', 'theories', 'among', 'general', 'public', 'such', 'reports', 'reduce', 'legitimacy', 'scientific', 'discoveries', 'regarding', 'cure', 'vaccine', 'covid', 'this']
['also', 'create', 'social', 'stigma', 'resulting', 'xenophobia', 'anti', 'chinese', 'sentiment', 'racism', 'marginalization', 'reduced', 'compliance', 'adherence', 'quarantine', 'have', 'adverse', 'health']
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[]
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['paper']
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[]
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['kalsoom', 'nawaz', 'hafiza', 'anum', 'saeed', 'tanveer', 'aslam', 'sajeel']
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['riphah', 'international', 'university', 'lahore', 'pakistan']
['clinical', 'psychologist', 'amina', 'abdullah', 'hospital', 'deplapur', 'okara', 'pakistan']
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['abstract']
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['mobilize', 'study', 'rapid', 'evaluation', 'treatment', 'community', 'level']
['international', 'journal', 'business', 'psychology']
['issue', '1ijbpsy', 'nawaz', '2020']
[]
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[]
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['lockdown', 'begin', 'again', 'according', 'author', 'best', 'knowledge']
['this', 'first', 'study', 'that', 'examines', 'asymmetrical', 'impact']
['covid', 'psychology', 'stock', 'markets', 'mostly']
['infected', 'nations', 'world', 'firstly', 'this', 'project', 'ascertained']
['stationary', 'level', 'study', 'variables', 'discovered', 'that']
['variables', 'stationary', 'stationary']
['hence', 'linear', 'version', 'autoregressive']
['distributed', 'ardl', 'model', 'applied']
['data', 'methodology']
['data']
['this', 'study', 'utilized', 'daily', 'data', 'number', 'confirmed']
['patients', 'deaths', 'covid', 'stock', 'markets']
['spain', 'italy', 'germany', 'from', 'period']
['february', '2020', 'april', '2020', 'this', 'study', 'taken', 'data']
['from', 'database', 'yahoo', 'finance', 'european', 'center']
['disease', 'control', 'prevention', 'further', 'this', 'investigation']
['analyzed', 'daily', 'returns', 'ibex', 'ftse']
['london', 'stock', 'exchange', 'nomination', 'spain', 'italy']
['germany', 'however', 'nasdaq', 'composite']
['index', 'represents']
['methodology']
['this', 'investigation', 'utilized', 'each', 'stock', 'market', 'daily', 'returns']
['dependent', 'variable', 'confirmed', 'cases', 'deaths']
['independent', 'variable', 'daily', 'returns', 'used', 'this', 'study']
['computed', 'follows', 'syllignakis', 'kouretas', '2011', 'shehzad']
['sohail', '2018']
['csti']
[]
['here']
['denote', 'return', 'value', 'time', 'closing']
['price', 'time', 'previous', 'closing', 'price', 'time']
['stock', 'market', 'respectively', 'linear', 'association', 'between', 'these']
['variables', 'defined']
['1sprt', 'β1usct', 'β2usdt']
['1nsrt', 'β1usct', 'β2usdt']
['frontiers', 'psychology', 'frontiersin', 'september', '2020', 'volume', 'article', '1924fpsyg', '01924', 'september', '2020', 'time']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['figure', 'deterioration', 'stock', 'markets', 'owing', 'novel', 'coronavirus']
['frontiers', 'psychology', 'frontiersin', 'september', '2020', 'volume', 'article', '1924fpsyg', '01924', 'september', '2020', 'time']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['figure', 'variations', 'global', 'financial', 'crisis', '2007', '2009']
['covid', 'crisis']
['1ibert', 'β1spct', 'β2spdt']
['1ftmrt', 'β1itlct', 'β2itldt']
['1daxrt', 'β1gerct', 'β2gerdt']
['1lsert', 'β1ukct', 'β2ukdt']
['where', 'signifies', 'first', 'difference', 'β0β1β2']
['independent', 'parameters', 'however', 'iber', 'ftmr']
['daxr', 'lser', 'defines', 'return', 'values', 'nasdaq']
['composite', 'index', 'ibex', 'ftse']
['london', 'stock', 'exchange', 'respectively', 'moreover']
['itlc', 'gerc', 'indicate', 'confirmed', 'cases', 'covid', 'spain', 'italy', 'germany', 'respectively']
['further', 'itld', 'gerd', 'refer', 'confirmed']
['deaths', 'covid', 'spain', 'italy', 'germany']
['respectively']
['ascertain', 'short', 'long', 'asymmetries', 'this']
['study', 'employed', 'linear', 'autoregressive', 'distributed']
['nardl', 'model', 'introduced', 'shin', '2014', 'this', 'model']
['performs', 'best', 'with', 'small', 'number', 'observations']
['applied', 'mixed', 'level', 'stationary', 'data']
['evaluates', 'linearity', 'cointegration', 'between', 'variables']
['equation', 'shared', 'short', 'variations', 'long']
['asymmetries', 'after', 'taking', 'long', 'parameters', 'into', 'account']
['shin', '2014', 'nardl', 'model', 'extended', 'form']
['ardl', 'pesaran', '2001', 'model', 'hence', 'full', 'form']
['nardl', 'model', 'each', 'stock', 'market', 'specified']
['1sprt']
[]
[]
[]
['1sprt']
[]
[]
[]
['1usc']
[]
[]
[]
[]
[]
['1usc']
[]
[]
[]
[]
[]
['1usd']
[]
[]
[]
[]
['1usd']
[]
['sprt', 'γ2ausc']
['γ2busc']
['γ3ausd']
[]
[]
[]
['1nsrt']
[]
[]
[]
['1nsrt']
[]
[]
[]
['1usc']
[]
[]
[]
[]
[]
['1usc']
[]
[]
[]
[]
[]
['1usd']
[]
[]
[]
[]
[]
[]
['γ1nsrt', 'γ2ausc']
['γ2busc']
[]
[]
['γ3busd']
[]
['1ibert']
[]
[]
[]
['1ibert']
[]
[]
[]
['1spc']
[]
[]
[]
[]
[]
['1spc']
[]
[]
[]
[]
[]
['1spd']
[]
[]
[]
[]
[]
[]
['γ1ibert', 'γ2aspc']
['γ2bspc']
[]
[]
['γ3bspd']
[]
['1ftmrt']
[]
[]
[]
['1ftmrt']
[]
[]
[]
['1itlc']
[]
[]
[]
[]
[]
['1itlc']
[]
[]
[]
[]
[]
['1itld']
[]
[]
[]
[]
[]
['itld']
['γ1ftmrt', 'γ2aitlc']
[]
['itlc']
[]
['itld']
[]
['itld']
[]
['1daxrt']
[]
[]
[]
['1daxrt']
[]
[]
[]
['1gerc']
[]
[]
[]
[]
[]
['1gerc']
[]
[]
[]
[]
['1gerd']
[]
[]
[]
[]
[]
['gerd']
['γ1daxrt', 'γ2agerc']
['γ2bgerc']
[]
['gerd']
['γ3bgerd']
[]
['frontiers', 'psychology', 'frontiersin', 'september', '2020', 'volume', 'article', '1924fpsyg', '01924', 'september', '2020', 'time']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['1lsert']
[]
[]
[]
['1lsert']
[]
[]
[]
['1ukc']
[]
[]
[]
[]
[]
['1ukc']
[]
[]
[]
[]
[]
['1ukd']
[]
[]
[]
[]
[]
[]
['γ1lsert', 'γ2aukc']
['γ2bukc']
[]
[]
['γ3bukd']
[]
['here', 'ϑ0ϑ1', 'ϑ2bϑ3aand', 'short', 'factors', 'while']
[]
[]
['indicates', 'long', 'parameters']
['whereas', 'symbolizes', 'number', 'used', 'model']
['based', 'akaik', 'information', 'criterion', 'schwarz', 'information']
['criterion', 'above', 'stated', 'equations', 'assumed', 'that']
['confirmed', 'number', 'cases', 'deaths', 'covid', 'have']
['asymmetrical', 'impact', 'stock', 'returns']
['thus']
[]
[]
[]
[]
[]
[]
[]
['itlc']
[]
[]
['itld']
[]
['gerc']
[]
['gerd']
[]
[]
[]
[]
[]
['designates', 'positive', 'shock', 'though']
[]
[]
[]
[]
[]
[]
[]
[]
['itlc']
[]
['itld']
[]
['gerc']
[]
['gerd']
[]
[]
[]
[]
[]
[]
['implies', 'negative', 'shock', 'each', 'variable']
['computed', 'follows']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['here']
[]
['delineates', 'positive', 'shock', 'variable', 'denotes']
['asymmetric', 'distributive', 'error', 'correction', 'form']
['these', 'factors', 'written', 'follows']
['1sprt']
[]
[]
[]
['1sprt']
[]
[]
[]
['1usc']
[]
[]
[]
[]
[]
['1usc']
[]
[]
[]
[]
[]
['1usd']
[]
[]
[]
[]
[]
[]
['φ1ectt']
['1nsrt']
[]
[]
[]
['1nsrt']
[]
[]
[]
['1usc']
[]
[]
[]
[]
[]
['1usc']
[]
[]
[]
[]
[]
['1usd']
[]
[]
[]
[]
[]
[]
['φ1ectt']
['1ibert']
[]
[]
[]
['1ibert']
[]
[]
[]
['1spc']
[]
[]
[]
[]
[]
['1spc']
[]
[]
[]
[]
[]
['1spd']
[]
[]
[]
[]
[]
[]
['φ1ectt']
['1ftmrt']
[]
[]
[]
['1ftmrt']
[]
[]
[]
['1itlc']
[]
[]
[]
[]
[]
['1itlc']
[]
[]
[]
[]
[]
['1itld']
[]
[]
[]
[]
[]
['itld']
['φ1ectt']
['1daxrt']
[]
[]
[]
['1daxrt']
[]
[]
[]
['1gerc']
[]
[]
[]
[]
[]
['1gerc']
[]
[]
[]
[]
[]
['1gerd']
[]
[]
[]
[]
[]
['gerd']
['φ1ectt']
['1lsert']
[]
[]
[]
['1lsert']
[]
[]
[]
['1ukc']
[]
[]
[]
[]
[]
['1ukc']
[]
[]
[]
[]
[]
['1ukd']
[]
[]
[]
[]
[]
[]
['φ1ectt']
['here', 'ectt', 'directs', 'error', 'correction', 'term', 'long']
['cointegration', 'among', 'variables', 'examined', 'through', 'bound']
['test', 'approach', 'pesaran', '2001', 'this', 'method', 'relies', 'test', 'evaluate', 'null', 'hypothesis']
['this', 'purpose', 'pesaran', '2001', 'defined', 'bounds']
['upper', 'lower', 'bound', 'estimated', 'statistics']
['higher', 'than', 'upper', 'bound', 'limit', 'then', 'null', 'hypothesis']
['cointegration', 'rejected', 'however', 'projected', 'value', 'test', 'lower', 'than', 'lower', 'bound', 'limit', 'then', 'null', 'hypothesis', 'cannot']
['rejected', 'besides', 'value', 'test', 'remains', 'between', 'both']
['limits', 'then', 'results', 'conclusive', 'this', 'project']
['employed', 'wald', 'test', 'verify', 'asymmetric', 'long']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['andshort']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['asymmetric']
['relationship', 'between', 'study', 'variables']
['frontiers', 'psychology', 'frontiersin', 'september', '2020', 'volume', 'article', '1924fpsyg', '01924', 'september', '2020', 'time']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['study', 'findings']
['descriptive', 'summary', 'showed', 'table', 'during', 'covid', 'mean', 'stock', 'returns', 'negative', 'further', 'skewness']
['values', 'these', 'markets', 'also', 'negative', 'except', 'lser', 'with']
['high', 'kurtosis', 'which', 'predicts', 'high', 'chances', 'loss', 'these', 'days']
['table', 'presents', 'results', 'augmented', 'dickey', 'fuller']
['test', 'dickey', 'fuller', '1979', 'phillip', 'perron', 'test']
['phillips', 'perron', '1988', 'showed', 'that', 'study', 'variables']
['have', 'diverse', 'stationary', 'levels', 'variable']
['cointegrated']
['bound', 'test', 'cointegration', 'fallouts']
['outcomes', 'bound', 'test', 'displayed', 'table', 'disclosed']
['significant', 'statistical', 'evidence', 'long', 'term', 'association', 'between']
['study', 'variables', 'each', 'equation', 'statistics', 'joint']
['significance', 'lagged', 'level', 'parameters', 'stated']
['respectively', 'these', 'values']
['surpass', 'upper', 'bound', 'limits', 'shin', '2014', 'implying']
['that', 'long', 'cointegration', 'exists', 'among', 'economic', 'crisis', 'health']
['crisis', 'financial', 'markets', 'each', 'equation']
['long', 'short', 'asymmetric']
['cointegration', 'reckoning']
['table', 'exhibited', 'upshots', 'wald', 'test', 'statistics']
['particularized', 'that', 'have', 'linear', 'association']
['with', 'however', 'revealed', 'short', 'asymmetric']
['affiliation', 'with', 'besides', 'caused', 'asymmetric']
['influence', 'iber', 'both', 'long', 'term', 'short', 'term', 'periods']
['while', 'possessed', 'short', 'asymmetry', 'only', 'additionally']
['table', 'descriptive', 'statistics']
['iber', 'lser']
['mean', '41737', '2222', '1463', '08997', '2083', '70833', '28187', '3658']
['skewness', '68003', '687765', '641313', '027343', '392073', '081854', '35422', '186996']
['kurtosis', '36907', '166164', '206693', '903565', '778953', '75376', '380136', '389965']
['jarque', 'bera', '7798', '26244', '69524', '87966', '1793', '8589', '47298', '76708']
['probability', '00434', '000161']
['gerc', 'gerd', 'daxr', 'itld', 'itlc', 'ftmr']
['mean', '3088', '18523', '1446', '54717', '42086', '9577', '1365', '40245']
['skewness', '927378', '53985', '120857', '230936', '65869', '232889', '058817', '31595']
['kurtosis', '43888', '59924', '573906', '120135', '135106', '868864', '472667', '04434']
['jarque', 'bera', '9095', '44518', '49843', '45175', '59309', '03773', '08892', '3192']
['probability', '000013', '003185', '000121', '000872']
['source', 'author', 'calculation']
['table', 'unit', 'root', 'test']
['statistics', 'statistics']
['variable', 'level', 'diff', 'level', 'diff']
['daxr', '893713', '54426', '057616', '2059']
['gerc', '293963', '969976', '971366', '51473']
['gerd', '935247', '359161', '147762', '393867']
['ftmr', '631659', '55646', '451458', '1619']
['itld', '127702', '174362', '400067', '35456']
['itlc', '300231', '849862', '488626', '372171']
['iber', '27056', '985616', '446574', '65801']
['506727', '32241', '159422', '09496']
['552953', '028486', '374729', '97616']
['lser', '775255', '591517', '768878', '87539']
['816325', '578091', '746256', '61778']
['429147', '36282', '571441', '31254']
['89506', '022076', '13962', '8073']
['847753', '567572', '310638', '46537']
['138569', '097646', '384682', '006879']
['66594', '40744', '64235', '28961']
['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation']
['frontiers', 'psychology', 'frontiersin', 'september', '2020', 'volume', 'article', '1924fpsyg', '01924', 'september', '2020', 'time']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['table', 'results', 'bound', 'test']
['test', 'statistic', 'value', 'signif', 'test', 'statistics', 'value', 'signif']
['ftmr']
['statistic', '02410', 'statistic', '165881']
[]
[]
['daxr']
['statistic', '11238', 'statistic', '23325']
[]
[]
['iber', 'lser']
['statistic', '490181', 'statistic', '03259']
[]
[]
['source', 'author', 'calculation']
['table', 'wald', 'test']
['long', 'asymmetry', 'short', 'asymmetry', 'long', 'asymmetry', 'short', 'asymmetry']
['stat', 'stat', 'ftmr', 'stat', 'stat']
['242605', '2916']
['1767']
['daxr']
['574046', 'gerc', 'gerc']
['589403', '609162', 'gerd', '338933', 'gerd']
['iber', 'lser']
['569179', '8616', '725822', '640751']
['61007', '8974', '786146', '718125']
['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation']
['results', 'ftmr', 'designated', 'that', 'itld', 'long', 'asymmetric']
['relationship', 'with', 'wald', 'statistics', 'gerc', 'enlightened']
['that', 'both', 'long', 'short', 'linear', 'impacts', 'daxr']
['gerd', 'short', 'term', 'linear', 'affiliation', 'with', 'daxr']
['moreover', 'denotes', 'long', 'asymmetric', 'liaison', 'with']
['lser', 'hence', 'existence', 'linearity', 'study', 'variables']
['choose', 'nardl', 'model', 'computing', 'long']
['short', 'factors']
['modeling', 'nardl', 'parameters']
['linear', 'fallouts', 'presented', 'table', 'illustrated']
['that', 'expansion', 'contraction', 'brings', 'reduction']
['increase', 'respectively', 'indicates', 'that', 'strict', 'lockdown']
['substantially', 'decreases', 'market', 'returns']
['vice', 'versa', 'further', 'positive', 'shock', 'caused', 'significant']
['rise', 'short', 'lagged', 'values', 'specified', 'that']
['negative', 'shock', 'amplified', 'besides', 'lagged']
['terms', 'described', 'that', 'uptick', 'effect', 'improved']
['also', 'negative', 'shock', 'second', 'augmented']
['first', 'third', 'represents', 'reverse', 'impact']
['furthermore', 'negative', 'significant', 'value', 'ectt', 'stated']
['that', 'disequilibrium', 'occurred', 'today', 'covid']
['will', 'adjust', 'with', 'speed', 'units', 'subsequent']
['squared', 'value', 'indicated', 'that', 'volatility']
['owing', 'covid']
['model', 'results', 'exhibited', 'table', 'particularized']
['that', 'both', 'positive', 'negative', 'shocks', 'escalate']
['while', 'enlargement', 'reduction', 'instigate', 'decrease']
['increase', 'specifies', 'that', 'upsurge', 'health', 'crisis']
['expressively', 'distressed', 'nasdaq', 'composite', 'index']
['vice', 'versa', 'nonetheless', 'short', 'negative', 'shock']
['demonstrated', 'imperative', 'decrease']
['whereas', 'expansion', 'lessens', 'negative']
['significant', 'ectt', 'parameter', 'stated', 'that', 'financial', 'instability']
['generated', 'covid', 'would', 'settled', 'with', 'speed']
['units', 'next', 'square', 'value', 'signified', 'that']
['instability', 'occurring', 'result', 'covid']
['finding', 'parameters', 'iber', 'model', 'table', 'identified']
['that', 'assertive', 'shock', 'encouraging', 'effect', 'iber']
['besides', 'adverse', 'shocks', 'remain', 'insignificant', 'other', 'hand']
['escalation', 'decline', 'negative', 'positive', 'influence']
['iber', 'implying', 'that', 'strict', 'lockdown', 'considerably', 'caused']
['financial', 'crisis', 'spain', 'long', 'term', 'period', 'short', 'term']
['period', 'intensification', 'decline']
['increased', 'iber', 'negative', 'significant', 'value', 'ectt']
['elucidated', 'that', 'today', 'disequilibrium', 'iber', 'will', 'adjust']
['next', 'with', 'speed', 'units', 'coefficient', 'square']
['stated', 'that', 'variations', 'iber', 'owing', 'covid']
['table', 'displayed', 'fallouts', 'ftse', 'stock', 'market']
['condition', 'covid', 'italy', 'stock', 'markets', 'exposed']
['frontiers', 'psychology', 'frontiersin', 'september', '2020', 'volume', 'article', '1924fpsyg', '01924', 'september', '2020', 'time']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['table', 'asymmetric', 'parameters']
['variables', 'short', 'term', 'coefficients', 'error', 'stat', 'prob']
['usc_neg', '047867', '003539', '52394']
['usc_neg', '061323', '003402', '02596']
['usc_neg', '108895', '005569', '55339']
['usc_neg', '072915', '003619', '14618']
['usd_pos', '713848', '036514', '55011']
['usd_pos', '23431', '014268', '42276']
['usd_pos', '194744', '01369', '22562']
['usd_pos', '334891', '029691', '27907']
['usd_neg', '377433', '055371', '816426']
['usd_neg', '65514', '097958', '89641']
['usd_neg', '074692', '02737', '728986']
['usd_neg', '650475', '235321', '51275']
['ectt', '369525', '066813', '49782']
['squared', '912909']
['adjusted', 'squared', '892007']
['durbin', 'watson', 'stat', '006439']
['variables', 'long', 'term', 'coefficients', 'error', 'stat', 'prob']
['usc_pos', '004491', '001667', '694157', '0099']
['usc_neg', '054381', '014406', '774912', '0005']
['usd_pos', '064436', '029631', '174626']
['usd_neg', '290139', '338048', '858278', '3953']
['181416', '264851', '684971', '4969']
['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation']
['table', 'asymmetric', 'parameters']
['variables', 'short', 'term', 'coefficients', 'error', 'stat', 'prob']
['usd_neg', '039324', '019503', '016267', '0487']
['usc_pos', '002147', '000382', '622014']
['usc_neg', '002319', '00069', '360414', '0014']
['usc_neg', '008198', '001064', '703282']
['ectt', '501702', '093855', '00026']
['squared', '812596']
['adjusted', 'squared', '800102']
['durbin', 'watson', 'stat', '130245']
['variables', 'long', 'term', 'coefficients', 'error', 'stat', 'prob']
['usd_pos', '013042', '004465', '920922', '0051']
['usd_neg', '026413', '030567', '864107', '3913']
['usc_pos', '001013', '00034', '977381', '0043']
['usc_neg', '004258', '001923', '214416']
['525513', '274541', '914153', '0608']
['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation']
['that', 'rise', 'reduction', 'itld', 'diminished', 'amplified', 'ftmr']
['inferring', 'that', 'heath', 'crisis', 'imperatively', 'contributes', 'italy']
['financial', 'instability', 'long', 'term', 'period', 'addition', 'growth']
['itlc', 'enlarged', 'ftmr', 'imperatively', 'sort', 'condition']
['italy', 'denoted', 'that', 'first', 'second', 'third', 'values', 'ftmr']
['harmed', 'returns', 'also', 'growth', 'itlc', 'negative']
['impression', 'ftmr', 'nonetheless', 'negative', 'shock']
['second', 'value', 'itlc', 'indicated', 'encouraging', 'effect']
['ftmr', 'consequences', 'itld', 'symbolized', 'that', 'proliferation']
['itld', 'possess', 'negative', 'sway', 'ftmer', 'first']
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[]
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[]
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[]
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[]
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[]
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[]
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[]
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[]
['sagar', 'joglekar1']
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[]
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[]
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[]
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[]
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[]
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[]
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[]
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[]
[]
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[]
[]
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[]
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[]
[]
[]
[]
[]
[]
[]
[]
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[]
[]
['mentions', 'medical', 'entities']
[]
[]
['interaction', 'types']
[]
[]
[]
['foursquare', 'visits']
[]
[]
[]
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[]
[]
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[]
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[]
[]
[]
[]
[]
[]
[]
[]
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[]
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[]
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[]
['they']
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[]
[]
['affiliation']
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[]
['motion']
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[]
[]
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[]
[]
[]
[]
['custom', 'words']
[]
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[]
[]
[]
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[]
['support', 'power', 'conflict']
[]
[]
[]
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[]
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[]
[]
[]
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[]
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[]
['stay', 'home']
[]
['grocery', 'travel', 'transport', 'outdoors', 'recreation']
[]
[]
[]
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[]
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['date', 'with', 'this', 'adaptable', 'group', 'individuals', 'future']
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['résumé']
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['formation', 'canada', 'conseil', 'canadien', 'programmes']
['psychologie', 'professionnelle', 'ccppp', 'groupe', 'agrément']
['société', 'canadienne', 'psychologie', 'association', 'organisa', 'tions', 'canadiennes', 'réglementation', 'psychologie', 'aocrp']
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['principaux', 'organismes', 'éducatifs', 'défense', 'droits', 'régle', 'mentation', 'associés', 'formation', 'psychologie', 'canada']
['défis', 'formation', 'liés', 'pandémie', 'élaboration', 'cadre']
['prise', 'décisions', 'pour', 'contexte', 'canadien', 'pouvant', 'aider']
['atténuer', 'défis', 'principes', 'directeurs', 'ambitieux']
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['exemples', 'personnels', 'impact', 'pandémie', 'rôles']
['organisations', 'affiliées', 'bien', 'pandémie', 'covid']
['posé', 'nombreux', 'défis', 'pour', 'formation', 'professionnelle', 'elle']
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['télésanté', 'pandémie', 'favorisé', 'meilleure', 'communication']
['sein', 'communauté', 'formation', 'entière', 'prises', 'décision']
['éthiques', 'fondées', 'consensus', 'nous', 'encouragés', 'définir']
['priorités', 'professionnelles', 'communauté', 'formation']
['psychologie', 'professionnelle', 'confrontée', 'problèmes']
['complexes', 'multiples', 'facettes', 'suite', 'pandémie']
['compte', 'nous', 'espérons', 'sortir', 'pandémie']
['covid', 'tant', 'communauté', 'formation', 'professionnelle']
['psychologie', 'plus', 'résiliente', 'plus', 'réfléchie', 'plus', 'cohésive']
['mots', 'clés', 'psychologie', 'clinique', 'psychologie', 'counseling', 'cation', 'prestataires', 'services', 'santé', 'psychologie', 'psycholo', 'scolaire']
['paper']
['commentary', 'reflections', 'covid']
['pandemic', 'health', 'disparities', 'pediatric']
['psychology']
['jessica', 'valenzuela', 'lori', 'crosby']
['psyd']
['roger', 'harrison']
[]
['college', 'psychology', 'nova', 'southeastern', 'university']
['division', 'behavioral', 'medicine', 'clinical', 'psychology']
['cincinnati', 'children', 'hospital', 'medical', 'center']
['college', 'medicine', 'university', 'cincinnati']
['nemours', 'alfred']
['dupont', 'hospital', 'children']
['sydney', 'kimmel', 'medical', 'college', 'thomas', 'jefferson', 'university']
['correspondence', 'concerning', 'this', 'article', 'should', 'addressed', 'lori', 'crosby', 'psyd', 'division', 'behavioral']
['medicine', 'clinical', 'psychology', 'cincinnati', 'children', 'hospital', 'medical', 'center', '3333', 'burnet', 'avenue', '7039']
['cincinnati', '45229', 'mail', 'lori', 'crosby', 'cchmc']
['received', '2020', 'revisions', 'received', 'july', '2020', 'accepted', 'july', '2020']
['abstract']
['covid', '2019', 'novel', 'coronavirus', 'pandemic', 'significant', 'economic', 'social', 'tional', 'public', 'health', 'impact', 'united', 'states', 'disturbing', 'trend', 'that', 'black', 'indigenous']
['people', 'color', 'bipoc', 'disproportionately', 'contracting', 'coronavirus', 'well', 'dying']
['from', 'covid', 'objective', 'methods', 'pandemic', 'potential', 'entrench', 'magnify', 'exist', 'health', 'disparities', 'families', 'marginalized', 'across', 'multiple', 'demographic', 'intersections', 'such']
['race', 'ethnicity', 'class', 'immigration', 'status', 'especially', 'vulnerable', 'these', 'inequities', 'have', 'been', 'ther', 'underscored', 'recent', 'murders', 'black', 'americans', 'police', 'resulting', 'spotlight']
['racial', 'injustice', 'united', 'states', 'results', 'efforts', 'lessen', 'spread', 'virus', 'have', 'resulted']
['changes', 'pediatric', 'primary', 'subspecialty', 'service', 'delivery', 'which', 'affect', 'access']
['bipoc', 'communities', 'bipoc', 'trainees', 'including', 'those', 'with', 'debt', 'caregiving', 'responsibilities']
['faced', 'with', 'barriers', 'resulting', 'delays', 'completion', 'their', 'training', 'further', 'clinical']
['community', 'based', 'translational', 'research', 'been', 'disrupted', 'heightened', 'safety', 'precautions']
['social', 'distancing', 'which', 'affect', 'bipoc', 'representation', 'research', 'downstream', 'conclusion']
['roles', 'clinicians', 'supervisors', 'trainees', 'researchers', 'primary', 'subspecialty', 'care']
['well', 'academia', 'pediatric', 'psychologists', 'have', 'ethical', 'responsibility', 'address', 'dispro', 'portionate', 'burden', 'this', 'pandemic', 'vulnerable', 'communities', 'allocate', 'time']
['resources', 'ensuring', 'health', 'equity', 'aftermath', 'covid']
['words', 'clinical', 'economic', 'disadvantage', 'race', 'ethnicity', 'research', 'training']
['introduction']
['covid', 'pandemic', 'affecting', 'segments']
['life', 'united', 'states', 'economic', 'social', 'emotional']
['public', 'health', 'every', 'state', 'territory']
['experienced', 'significant', 'numbers', 'cases', 'deaths']
['covid', 'disturbing', 'trend', 'persisted']
['states', 'report', 'data', 'illness', 'hospitalizations']
['deaths', 'covid', 'black', 'indigenous']
['people', 'color', 'bipoc', 'disproportionately', 'tracting', 'coronavirus', 'well', 'dying', 'from', 'covid']
['centers', 'disease', 'control', 'prevention', '2020']
['disparate', 'impact', 'coronavirus', 'bipoc']
['been', 'linked', 'preexisting', 'inequities', 'including']
['poorer', 'access', 'healthcare', 'wage', 'employment']
['author', '2020', 'published', 'oxford', 'university', 'press', 'behalf', 'society', 'pediatric', 'psychology']
['rights', 'reserved', 'permissions', 'please', 'mail', 'journals', 'permissions']
['journal', 'pediatric', 'psychology', '2020']
['1093', 'jpepsy', 'jsaa063']
['advance', 'access', 'publication', 'date', 'august', '2020']
['commentary']
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['more', 'fortuna', '2020']
['noted', 'current', 'president', 'american']
['psychological', 'association', '2020', 'covid']
['pandemic', 'occurring', 'middle', 'racism', 'demic', 'highlighting', 'psychological', 'anxiety']
['depression', 'traumatic', 'stress', 'disorders', 'physical']
['health', 'toll', 'racism', 'consequently', 'bipoc', 'communi', 'ties', 'struggling', 'manage', 'effects', 'covid']
['well', 'burden', 'racial', 'injustice']
['underscored', 'recent', 'murders', 'black', 'citizens']
['police', 'nationwide', 'protests', 'against', 'injustice']
['police', 'brutality']
['covid', 'pandemic', 'significant']
['changes', 'pediatric', 'primary', 'subspecialty', 'service']
['delivery', 'including', 'ability', 'pediatric', 'psycholo', 'gists', 'remain', 'integrated', 'medical', 'settings', 'part']
['efforts', 'flatten', 'curve', 'halt', 'spread']
['infections', 'pediatric', 'healthcare', 'providers', 'have', 'limited']
['office', 'visits', 'implemented', 'social', 'distancing', 'meas', 'ures', 'during', 'visits', 'migrated', 'digital', 'telehealth']
['platforms', 'many', 'visits', 'that', 'would', 'ordinarily', 'result']
['referral', 'warm', 'handoff', 'psychology']
['although', 'using', 'these', 'platforms', 'provide', 'patient']
['care', 'holds', 'promise', 'increasing', 'access', 'under', 'served', 'there', 'some', 'evidence', 'problematic', 'inequity']
['accessibility', 'acceptability', 'such', 'platforms']
['vulnerable', 'families', 'have', 'limited', 'access', 'ternet', 'service', 'compatible', 'devices', 'support', 'tele', 'health', 'visits', 'families', 'with', 'young', 'children']
['have', 'caregiver', 'present', 'during', 'because']
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[]
['department', 'psychology', 'virginia', 'commonwealth', 'university', 'richmond', 'virginia']
[]
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[]
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[]
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[]
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[]
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[]
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[]
['yitian']
[]
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['qiujie']
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[]
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[]
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['support', 'well', 'thanks', 'this', 'course', 'understood']
['importance', 'funeral', 'ritual', 'closeness', 'support']
['community', 'that', 'time', 'mourners', 'never']
['asked', 'myself', 'question', 'rather', 'seemed', 'unimportant']
['finally', 'understood', 'importance', 'meaning', 'this']
['celebration', 'even', 'though', 'believer', 'agreeing', 'with']
['lorenzo', 'years', 'said', 'think', 'that', 'course', 'helped']
['above', 'give', 'words', 'something', 'that', 'already', 'only', 'sensed']
['that', 'value', 'rituality', 'death', 'seeing', 'this']
['been', 'made', 'impossible', 'rules', 'social', 'distancing']
['anguish', 'dismay', 'anger', 'that', 'this', 'caused', 'population']
['like', 'meaning', 'value', 'this', 'experience']
['lastly', 'this', 'thematic', 'area', 'roberta', 'also', 'highlighted', 'another']
['fundamental', 'aspect', 'that', 'difference', 'between', 'religiosity']
['spirituality', 'which', 'turn', 'allowed', 'feel', 'freer', 'explore']
['latter', 'personal', 'dimension', 'understanding', 'that']
['spiritual', 'dimension', 'different', 'from', 'religiosity', 'what', 'revolves']
['around', 'religion', 'finally', 'allowed', 'open', 'dialogue', 'with']
['myself', 'reflect', 'deeply', 'coordinates', 'life', 'being', 'able']
['this', 'this', 'time', 'pandemic', 'important']
['third', 'prevalent', 'thematic', 'area']
['significance', 'death', 'education', 'future']
['health', 'professionals']
['awareness', 'having', 'realistically', 'reckon', 'with', 'limit']
['particular', 'with', 'limit', 'life', 'foreground']
['pandemic', 'well', 'paralyzing', 'lack', 'proper']
['rites', 'support', 'mourners', 'those', 'moments', 'allowed']
['participants', 'also', 'recognize', 'turn', 'positive', 'aspects']
['sharing', 'thoughts', 'opinions', 'feelings', 'within', 'educational', 'paths']
['that', 'allow', 'place', 'death', 'historical', 'cultural', 'context', 'especially']
['moments', 'extreme', 'uncertainly', 'present', 'covid']
['related', 'participants', 'indeed', 'expressed', 'that', 'they', 'believed']
['proper', 'death', 'education', 'paths', 'could', 'represent', 'valuable', 'tool']
['strengthen', 'people', 'psychological', 'resources', 'help', 'them', 'face']
['these', 'complex', 'situations', 'example', 'according', 'alberto']
['looking', 'death', 'tracing', 'historical', 'symbolic', 'cultural']
['scientific', 'psychological', 'path', 'allowed', 'acquire', 'greater']
['awareness', 'greater', 'critical', 'sense', 'about', 'events', 'that']
['especially', 'during', 'acute', 'phase', 'pandemic', 'congested']
['windows', 'media', 'more', 'specifically', 'course', 'death']
['education', 'helped', 'some', 'participants', 'better', 'understand']
['formulate', 'their', 'understanding', 'death']
['example', 'chiara', 'years', 'expressed', 'study', 'themes']
['related', 'death', 'dying', 'given', 'opportunity', 'better']
['understand', 'death', 'been', 'understood', 'during', 'various']
['eras', 'this', 'become', 'more', 'aware', 'understand']
['death', 'what', 'value', 'attribute', 'giving', 'opportunity']
['reflect', 'relate', 'idea', 'death', 'especially', 'sudden']
['death', 'seems', 'that', 'this', 'helps', 'better', 'manage', 'what']
['living', 'valeria', 'seemed', 'echo', 'this', 'course', 'given']
['tools', 'overcome', 'that', 'step', 'anguish', 'that', 'makes', 'death', 'fearful']
['unthinkable', 'taught', 'look', 'theme']
['entirety', 'without', 'taking', 'anything', 'granted']
['relating', 'this', 'luigi', 'years', 'expressed', 'well']
['course', 'helped', 'understand', 'pandemic', 'situation', 'live']
['with', 'more', 'maturity', 'know', 'more', 'serene', 'than']
['would', 'normally', 'have', 'been', 'certainly', 'opening', 'eyes', 'from']
['scientific', 'point', 'view', 'topics', 'covered', 'course']
['basis', 'reading', 'this', 'situation', 'with', 'maturity', 'awareness']
['according', 'lucia', 'during', 'course', 'finally', 'able']
['face', 'fear', 'talk', 'about', 'death', 'always', 'precisely']
['this', 'period', 'have', 'given', 'shape', 'this', 'fear', 'talking', 'about', 'death']
['able', 'understand', 'origin', 'great', 'shortcomings']
['that', 'there', 'society', 'according', 'participants']
['being', 'able', 'understand', 'topics', 'this', 'magnitude', 'allows']
['treat', 'them', 'from', 'more', 'objective', 'point', 'view', 'with']
['less', 'emotional', 'involvement', 'example', 'elisa', 'years']
['frontiers', 'public', 'health', 'frontiersin', 'april', '2021', 'volume', 'article', '625756testoni', 'psychology', 'students', 'perceptions', 'covid']
['explained', 'what', 'helped', 'most', 'hearing', 'about', 'death', 'with']
['critical', 'scholar', 'helped', 'make', 'topic', 'more']
['normal', 'more', 'treatable', 'this', 'fact', 'allowed', 'distance']
['myself', 'from', 'fear', 'that', 'death', 'inevitably', 'implies', 'positivity']
['course', 'also', 'linked', 'fact', 'that', 'allowed', 'focus']
['life', 'recognize', 'importance', 'deserves', 'dealing', 'with']
['these', 'issues', 'manuela', 'years', 'said', 'makes', 'possible']
['give', 'more', 'relevance', 'meaning', 'significance', 'lived']
['existence', 'rather', 'than', 'fear', 'finitude', 'life', 'while', 'very']
['similar', 'federica', 'years', 'expressed', 'herself', 'talking']
['about', 'death', 'gives', 'meaning', 'life', 'makes', 'feel', 'more', 'alive']
['this', 'course', 'allowed', 'face', 'lockdown', 'valuing']
['every', 'moment']
['discussion']
['present', 'study', 'which', 'been', 'summarized', 'from']
['elaboration', 'main', 'results', 'figure', 'aimed', 'explore']
['psychology', 'students', 'experiences', 'perspectives', 'concerning']
['present', 'covid', 'related', 'mortality', 'salience', 'situation']
['especially', 'lockdown', 'measures', 'light', 'death', 'education']
['course', 'they', 'attended', 'concerning', 'theme', 'death']
['life', 'international', 'studies', 'have', 'indeed', 'already', 'highlighted']
['other', 'healthcare', 'students', 'especially', 'medical', 'nursing']
['ones', 'report', 'feel', 'adequately', 'prepared', 'face', 'these', 'issues']
['support', 'dying', 'patient', 'intense', 'need']
['proper', 'death', 'education', 'palliative', 'care', 'trainings', 'indeed', 'still']
['extremely', 'high']
['current', 'covid', 'pandemic', 'exacerbated', 'this']
['critical', 'issue', 'causing', 'intense', 'feelings', 'uncertainty', 'high']
['distress', 'among', 'healthcare', 'students', 'face', 'generally']
['unprepared', 'unprecedented', 'sanitary', 'crisis', 'with', 'extremely']
['high', 'deathrates']
['present', 'research', 'findings', 'have', 'highlighted', 'these', 'critical']
['aspects', 'well', 'relation', 'psychology', 'students', 'concerning']
['whom', 'less', 'studies', 'have', 'been', 'conducted', 'live']
['learn', 'covid', 'period', 'deal', 'with']
['related', 'abrupt', 'increase', 'mortality', 'salience', 'while', 'attending']
['death', 'education', 'course', 'from', 'obtained', 'results', 'three', 'main']
['forms', 'discovering', 'that', 'characterized', 'students', 'feeling']
['learning', 'have', 'emerged', 'first', 'discovering', 'removal']
['death', 'contemporary', 'culture', 'second', 'importance']
['community', 'rituals', 'funeral', 'also', 'spirituality']
['third', 'significance', 'death', 'education', 'future']
['health', 'professionals']
['with', 'respect', 'first', 'thematic', 'area', 'most', 'important']
['feeling', 'students', 'experienced', 'become', 'aware', 'social']
['strategies', 'that', 'characterize', 'removal', 'death', 'from']
['consciousness', 'everyday', 'life', 'realizing', 'this']
['general', 'inability', 'deal', 'with', 'shocking', 'experience']
['pandemic', 'this', 'paralyzing', 'lack', 'adequate', 'skills', 'related']
['death', 'life', 'exacerbated', 'made', 'even', 'more']
['evident', 'pandemic', 'already', 'been', 'reported', 'other']
['healthcare', 'students', 'well', 'literature', 'especially', 'undergraduate']
['nurses', 'doctors', 'already', 'been', 'highlighted', 'have']
['reported', 'overwhelmingly', 'unprepared', 'they', 'felt', 'concerning']
['present', 'sanitary', 'emergency', 'think', 'that', 'study']
['contents', 'helped', 'participants', 'develop', 'broader']
['perspective', 'this', 'problem', 'some', 'students', 'also', 'reported']
['personal', 'experiences', 'very', 'specific', 'ones', 'that', 'omitted', 'this']
['report', 'because', 'they', 'could', 'make', 'participants', 'recognizable']
['these', 'testimonies', 'described', 'important', 'losses', 'unanswered']
['requests', 'needs', 'support', 'supporting', 'literature', 'addressed']
['importance', 'reflecting', 'fundamental', 'existential', 'themes']
['healthcare', 'students', 'order', 'acquire', 'broader', 'categories']
['thought', 'suitable', 'language', 'give', 'meaning', 'life']
['pain', 'loss', 'acquire', 'adequate', 'professional', 'skills', 'able']
['deal', 'with', 'these', 'themes', 'their', 'future']
['second', 'thematic', 'area', 'highlighted', 'further', 'discovery']
['significantly', 'related', 'first', 'that', 'importance']
['accompanying', 'rites', 'those', 'funerals', 'this']
['made', 'possible', 'attention', 'understand']
['suffering', 'those', 'deal', 'with', 'loss', 'loved']
['during', 'pandemic', 'without', 'being', 'able', 'accompany']
['without', 'being', 'able', 'celebrate', 'appropriate']
['funeral', 'ritual', 'this', 'line', 'with', 'other', 'literature', 'findings']
['which', 'have', 'highlighted', 'healthcare', 'professionals', 'students']
['trainees', 'general', 'felt', 'painful', 'burden', 'having']
['assist', 'support', 'patients', 'were', 'indeed', 'dying', 'alone']
['support', 'family', 'members', 'could', 'properly', 'accompany']
['later', 'mourn', 'their', 'loved', 'this', 'scenario']
['reflection', 'spiritual', 'dimension', 'took', 'shape', 'until', 'then']
['underestimated', 'this', 'been', 'linked', 'students', 'their']
['need', 'closeness', 'with', 'others', 'sharing', 'negative', 'feelings']
['caused', 'pandemic', 'since', 'forced', 'isolation', 'made', 'them', 'feel']
['isolated', 'moment', 'most', 'intense', 'fear', 'therefore', 'they']
['identified', 'themselves', 'with', 'those', 'hospitals', 'could']
['contact', 'with', 'their', 'loved', 'ones', 'course', 'therefore']
['allowed', 'students', 'discover', 'importance', 'funeral', 'rite']
['spirituality', 'allowing', 'some', 'reconnect', 'explore', 'their']
['inner', 'dimension', 'regardless', 'their', 'attitude', 'toward', 'religion']
['other', 'studies', 'literature', 'have', 'indeed', 'highlighted']
['essential', 'healthcare', 'students', 'believe', 'receiving', 'proper', 'training']
['also', 'concerning', 'spiritual', 'aspects', 'patients', 'care']
['contrary', 'current', 'lack', 'information', 'reflection', 'upon']
['these', 'themes', 'healthcare', 'university', 'courses']
['lastly', 'last', 'thematic', 'area', 'concerned', 'indeed', 'importance']
['death', 'education', 'courses', 'university', 'students', 'will']
['pursue', 'careers', 'healthcare', 'future', 'issues', 'that']
['comprised', 'this', 'theme', 'were', 'acquisition', 'awareness']
['personal', 'reflection', 'that', 'allow', 'recognition', 'control']
['negative', 'emotions', 'that', 'death', 'dying', 'arouse', 'consequently']
['they', 'also', 'allow', 'people', 'focus', 'attention', 'life', 'through']
['dissolution', 'taboos', 'related', 'these', 'topics', 'they', 'have', 'become']
['aware', 'learned', 'recognize', 'negative', 'emotions', 'that']
['occur', 'situations', 'related', 'death', 'finally', 'positivity']
['course', 'substantially', 'derived', 'from', 'focus', 'life']
['recognition', 'importance', 'general', 'participants']
['recognized', 'need', 'adequate', 'preparation', 'regarding']
['topics', 'covered', 'course', 'only', 'live', 'better', 'deal']
['with', 'these', 'issues', 'more', 'serenely', 'also', 'their', 'future', 'health']
['professionals', 'participants', 'therefore', 'expressed', 'only', 'positive']
['psychological', 'educational', 'effects', 'valuing', 'much']
['course', 'allowed', 'them', 'better', 'manage', 'social', 'trauma']
['frontiers', 'public', 'health', 'frontiersin', 'april', '2021', 'volume', 'article', '625756testoni', 'psychology', 'students', 'perceptions', 'covid']
['figure', 'findings', 'qualitative', 'data', 'analysis', 'main', 'themes', 'their', 'relationships']
['caused', 'covid', 'this', 'appears', 'line', 'with', 'other']
['literature', 'especially', 'studies', 'involving', 'also', 'other', 'healthcare']
['students', 'well', 'which', 'have', 'confirmed', 'important']
['implement', 'death', 'education', 'pathways', 'university', 'students']
['especially', 'those', 'studying', 'become', 'health', 'professionals']
['present', 'results', 'highlighted', 'well']
['conclusions']
['present', 'study', 'highlighted', 'death', 'education', 'course']
['conducted', 'during', 'first', 'phase', 'covid', 'pandemic']
['italy', 'helped', 'students', 'become', 'more', 'aware', 'issues', 'addressed']
['through', 'contents', 'dialogues', 'that', 'encouraged', 'participants']
['develop', 'their', 'critical', 'thinking', 'allowed', 'them', 'rediscover']
['value', 'reflection', 'sense', 'limit', 'finitude', 'life']
['different', 'representations', 'death', 'since', 'these', 'meanings']
['usually', 'taken', 'granted', 'despite', 'general', 'lack', 'awareness']
['about', 'representations', 'death', 'possibility', 'freely', 'reflect']
['upon', 'discuss', 'these', 'issues', 'offered', 'course', 'rendered']
['therefore', 'significant', 'tool', 'support', 'participants', 'during']
['confusing', 'highly', 'dramatic', 'period', 'pandemic']
['related', 'this', 'research', 'results', 'showed', 'also', 'that', 'course']
['helped', 'participants', 'consider', 'religious', 'spiritual', 'aspect']
['corresponding', 'distal', 'defenses', 'indicated']
['indeed', 'line', 'with', 'perspective', 'religious', 'spiritual']
['vision', 'mitigates', 'concerns', 'that', 'arise', 'from', 'awareness']
['death', 'allows', 'people', 'focus', 'their', 'attention', 'everyday', 'life']
['without', 'being', 'assailed', 'death', 'anxiety', 'another', 'fundamental']
['element', 'that', 'emerged', 'from', 'research', 'been']
['rediscovered', 'importance', 'funeral', 'rites', 'maintain', 'sense']
['community', 'especially', 'while', 'facing', 'extremely', 'painful', 'historical']
['periods', 'significantly', 'characterized', 'grief', 'study', 'highlighted']
['indeed', 'concrete', 'need', 'address', 'these', 'problems', 'link']
['them', 'wider', 'existential', 'issues', 'such', 'ability', 'improve']
['reflection', 'meaning', 'life', 'address', 'sense', 'limit']
['these', 'results', 'confirm', 'once', 'again', 'effectiveness', 'reflection']
['death', 'dying', 'critical', 'periods', 'such', 'covid']
['pandemic', 'because', 'permits', 'awareness', 'unconscious', 'aspects']
['that', 'characterize', 'terror', 'death', 'gives', 'them', 'cultural']
['relative', 'dimensions', 'this', 'especially', 'important', 'healthcare']
['professionals', 'already', 'find', 'generally', 'difficult', 'rather']
['distressing', 'manage', 'life', 'patient', 'lack']
['proper', 'training', 'these', 'aspects', 'therefore', 'have']
['face', 'this', 'pandemic', 'period', 'additional', 'psychological']
['burden', 'dealing', 'with', 'even', 'higher', 'mortality', 'salience', 'lack']
['proper', 'personal', 'professional', 'tools', 'elaborate', 'break']
['silence', 'taboo', 'that', 'generally', 'surrounds', 'experience']
['death', 'even', 'them', 'considering', 'this', 'covid', 'pandemic']
['extremely', 'high', 'number', 'deaths', 'their', 'massive', 'burden']
['healthcare', 'professionals', 'actively', 'deal', 'with', 'them', 'daily']
['basis', 'represent', 'significant', 'public', 'health', 'challenge', 'which']
['more', 'more', 'attention', 'should', 'given', 'near', 'future']
['given', 'current', 'study', 'findings', 'therefore', 'introduction']
['increase', 'death', 'education', 'pathways', 'university', 'courses']
['could', 'great', 'importance', 'especially', 'present', 'situation']
['frontiers', 'public', 'health', 'frontiersin', 'april', '2021', 'volume', 'article', '625756testoni', 'psychology', 'students', 'perceptions', 'covid']
['covid', 'pandemic', 'they', 'could', 'indeed', 'help', 'young', 'adults']
['obtain', 'proper', 'resources', 'personal', 'skills', 'they', 'need']
['adequately', 'face', 'this', 'period', 'intense', 'uncertainty', 'mortality']
['salience', 'other', 'future', 'challenge', 'there', 'might', 'store']
['especially', 'healthcare', 'students', 'like', 'psychologists', 'example']
['will', 'need', 'able', 'properly', 'address', 'these', 'themes', 'with']
['their', 'patients', 'near', 'future', 'proper', 'death', 'education', 'courses']
['should', 'therefore', 'implemented', 'keeping', 'mind', 'need']
['allow', 'students', 'openly', 'discuss', 'without', 'prejudices', 'fear']
['theme', 'death', 'human', 'vulnerability', 'express', 'related']
['emotions', 'through', 'mean', 'they', 'feel', 'could', 'helpful']
['limits', 'future', 'perspectives']
['research', 'reports', 'encouraging', 'results', 'that', 'demonstrate']
['positive', 'effects', 'death', 'education', 'however', 'some', 'limitations']
['should', 'taken', 'into', 'account', 'including', 'inability', 'generalize']
['results', 'given', 'specificity', 'conditions', 'concerning', 'this']
['future', 'research', 'should', 'focus', 'significantly', 'broaden', 'number']
['participants', 'addition', 'this', 'idea', 'longitudinal']
['study', 'considered', 'approach', 'discussion']
['existential', 'issues', 'addressed', 'actually', 'changed']
['kept', 'stable', 'over', 'time', 'moreover', 'since', 'research', 'participants']
['were', 'mostly', 'female', 'even', 'though', 'psychology', 'students']
['usually', 'more', 'female', 'than', 'male', 'future', 'research', 'could', 'explore']
['more', 'precisely', 'more', 'male', 'psychology', 'students', 'point', 'view']
['comparing', 'female', 'students', 'other', 'interesting']
['aspects', 'that', 'could', 'investigated', 'future', 'studies', 'could', 'also']
['perspective', 'medical', 'nursing', 'social', 'services', 'students']
['well', 'being', 'conducted', 'other', 'cultural', 'contexts', 'since']
['comparing', 'psychology', 'students', 'perspectives', 'point', 'view']
['other', 'healthcare', 'social', 'services', 'professionals', 'could']
['particularly', 'interesting', 'informative']
['data', 'availability', 'statement']
['data', 'supporting', 'conclusions', 'this']
['article', 'will', 'made', 'available', 'authors', 'without']
['undue', 'reservation']
['ethics', 'statement']
['studies', 'involving', 'human', 'participants', 'were']
['reviewed', 'approved', 'ethical', 'committee']
['psychological', 'research', 'university']
['padua', '57bc2002fdf5cbd4292f5f86aa077f23']
['patients', 'participants', 'provided', 'their', 'written', 'informed', 'consent']
['participate', 'this', 'study']
['author', 'contributions']
['contributed', 'research', 'design', 'project', 'planning']
['supervision', 'research', 'analysis', 'texts', 'methodology']
['article', 'writing', 'contributed', 'project', 'planning']
['analysis', 'texts', 'article', 'writing', 'contributed']
['interviews', 'analysis', 'texts', 'contributed']
['supervision', 'cooperation', 'organization', 'teamwork']
['writing', 'contributed', 'analysis', 'texts', 'cooperation']
['organization', 'team', 'work', 'contributed']
['writing', 'authors', 'contributed', 'article', 'approved']
['submitted', 'version']
['acknowledgments']
['thank', 'davide', 'viezzoli', 'marco', 'antonellini', 'alice', 'culcasi']
['gianmarco', 'biancalani', 'help', 'some', 'critical', 'phases']
['work']

# LSA Model
number_of_topics=100
words=word_count_psy
model_psy=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model_psy.show_topic(0, topn=words))##dict and encoding matrix values

#These variables are for further experimentation in vertical comparission mantel test
model_psy_paragraph=create_gensim_lsa_model(clean_text,number_of_topics,words)

#generating top 5 words form lsa
dict(model_psy.show_topic(0, topn=5))

{'2020': 0.5867463921244819,
 'covid': 0.4071883489033404,
 'psycholog': 0.3061861652069924,
 'pandem': 0.2779430420491234,
 'health': 0.23444970867587098}

#wordclod for top 10 words
from wordcloud import WordCloud
text = dict(model_psy.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph26.png')

# Cosine similarity matrix of top 5 words

a=pd.DataFrame()

for i in range(number_of_topics):
    words_psy =dict(model_psy.show_topic(i, topn=words))
    b=pd.DataFrame(words_psy,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_psy.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled29.png')

cosine-similarity
                  2020        covid    psycholog       pandem       health
2020                 1 -0.000181265 -0.000892643  0.000282476 -0.000811633
covid     -0.000181265            1 -3.26851e-05  0.000850608  -0.00243693
psycholog -0.000892643 -3.26851e-05            1 -0.000796692  -0.00297452
pandem     0.000282476  0.000850608 -0.000796692            1 -0.000958762
health    -0.000811633  -0.00243693  -0.00297452 -0.000958762            1

#networkx graph of top 5 words
df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *100  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='g',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig29.png')

#mantel test for 10 word network
mantel_test(5)

Pairwise-distance
               2020     covid psycholog    pandem       health      imag  \
2020              0   1.36666  0.934002   1.32086       1.2907   1.36248   
covid       1.36666         0  0.996012    1.3575      1.34232   1.40654   
psycholog  0.934002  0.996012         0  0.923107     0.897853  0.993867   
pandem      1.32086    1.3575  0.923107         0      1.29287   1.35564   
health       1.2907   1.34232  0.897853   1.29287  2.10734e-08   1.34081   
imag        1.36248   1.40654  0.993867   1.35564      1.34081         0   
model       1.36538   1.40674  0.993419   1.35499      1.33957   1.40463   
use         1.36557   1.40914  0.996611   1.35691      1.34055   1.40774   
detect      1.35037   1.39005  0.974971   1.34948      1.32741   1.39195   

              model       use    detect  
2020        1.36538   1.36557   1.35037  
covid       1.40674   1.40914   1.39005  
psycholog  0.993419  0.996611  0.974971  
pandem      1.35499   1.35691   1.34948  
health      1.33957   1.34055   1.32741  
imag        1.40463   1.40774   1.39195  
model             0   1.40712   1.39334  
use         1.40712         0   1.39394  
detect      1.39334   1.39394         0  
Pairwise-distance
                  2020        covid psycholog       pandem    health  \
2020       2.98023e-08      1.40877   1.40522      1.40738    1.3938   
covid          1.40877  2.10734e-08   1.40445      1.40681   1.39476   
psycholog      1.40522      1.40445         0      1.40392   1.39104   
pandem         1.40738      1.40681   1.40392  2.58096e-08   1.39265   
health          1.3938      1.39476   1.39104      1.39265         0   
imag           0.99628     0.997069  0.991081     0.995442  0.975322   
model           1.1505      1.15627   1.14737      1.14876   1.12853   
use            1.39346      1.39292   1.38969      1.39032   1.37669   
detect         0.99675     0.995804  0.990124     0.996323  0.975233   

                imag     model       use     detect  
2020         0.99628    1.1505   1.39346    0.99675  
covid       0.997069   1.15627   1.39292   0.995804  
psycholog   0.991081   1.14737   1.38969   0.990124  
pandem      0.995442   1.14876   1.39032   0.996323  
health      0.975322   1.12853   1.37669   0.975233  
imag               0   0.58304  0.975096  0.0511581  
model        0.58304         0   1.11509   0.584022  
use         0.975096   1.11509         0   0.972546  
detect     0.0511581  0.584022  0.972546          0  

(-0.31738225480066284, 0.9036, -0.9287861176988951)

#mantel test for 20 word network
mantel_test(10)

Pairwise-distance
               2020     covid  psycholog    pandem       health    social  \
2020              0   1.36666   0.934002   1.32086       1.2907   1.01582   
covid       1.36666         0   0.996012    1.3575      1.34232   1.08085   
psycholog  0.934002  0.996012          0  0.923107     0.897853  0.425651   
pandem      1.32086    1.3575   0.923107         0      1.29287   1.00801   
health       1.2907   1.34232   0.897853   1.29287  2.10734e-08  0.979298   
social      1.01582   1.08085   0.425651   1.00801     0.979298         0   
impact     0.950754  0.995943   0.157101   0.88269     0.884908  0.435621   
polici     0.937932  0.996663  0.0448366    0.9206     0.893999  0.426208   
student      1.2335   1.27581   0.794231   1.22584      1.21903  0.905925   
countri    0.950655   1.01329   0.185665  0.916894     0.889609  0.423086   
imag        1.36248   1.40654   0.993867   1.35564      1.34081   1.08009   
model       1.36538   1.40674   0.993419   1.35499      1.33957    1.0812   
use         1.36557   1.40914   0.996611   1.35691      1.34055   1.08552   
detect      1.35037   1.39005   0.974971   1.34948      1.32741   1.05601   
data        1.36114   1.40437   0.990025   1.35348      1.33455   1.08021   
dataset     1.35992   1.40386    0.99111   1.35622      1.34144   1.07603   
learn        1.3361   1.38052   0.955282   1.32546      1.31606    1.0256   
volum       1.14072   1.26091   0.780132   1.16492      1.18771  0.927891   
infect      1.35672    1.3992   0.983519   1.34927      1.33008     1.079   

             impact     polici      student   countri      imag     model  \
2020       0.950754   0.937932       1.2335  0.950655   1.36248   1.36538   
covid      0.995943   0.996663      1.27581   1.01329   1.40654   1.40674   
psycholog  0.157101  0.0448366     0.794231  0.185665  0.993867  0.993419   
pandem      0.88269     0.9206      1.22584  0.916894   1.35564   1.35499   
health     0.884908   0.893999      1.21903  0.889609   1.34081   1.33957   
social     0.435621   0.426208     0.905925  0.423086   1.08009    1.0812   
impact            0   0.153858     0.830717  0.205929   1.00867   1.00803   
polici     0.153858          0     0.798974  0.187909  0.995935  0.995262   
student    0.830717   0.798974  1.49012e-08  0.829769   1.27571   1.26995   
countri    0.205929   0.187909     0.829769         0    1.0149   1.01474   
imag        1.00867   0.995935      1.27571    1.0149         0   1.40463   
model       1.00803   0.995262      1.26995   1.01474   1.40463         0   
use         1.01226   0.996601      1.27769    1.0163   1.40774   1.40712   
detect     0.982811   0.976178      1.25587  0.992154   1.39195   1.39334   
data        1.00339   0.988378      1.27395  0.995645   1.40665   1.40225   
dataset     1.00507   0.993097      1.27018   1.01375    1.4054   1.40361   
learn      0.960717   0.953611      1.23661  0.974862   1.37823   1.37808   
volum      0.830664   0.785515      1.10202  0.811453   1.26349   1.26444   
infect     0.990138   0.984038      1.26395   1.00841   1.39841   1.39689   

                use    detect         data      dataset        learn  \
2020        1.36557   1.35037      1.36114      1.35992       1.3361   
covid       1.40914   1.39005      1.40437      1.40386      1.38052   
psycholog  0.996611  0.974971     0.990025      0.99111     0.955282   
pandem      1.35691   1.34948      1.35348      1.35622      1.32546   
health      1.34055   1.32741      1.33455      1.34144      1.31606   
social      1.08552   1.05601      1.08021      1.07603       1.0256   
impact      1.01226  0.982811      1.00339      1.00507     0.960717   
polici     0.996601  0.976178     0.988378     0.993097     0.953611   
student     1.27769   1.25587      1.27395      1.27018      1.23661   
countri      1.0163  0.992154     0.995645      1.01375     0.974862   
imag        1.40774   1.39195      1.40665       1.4054      1.37823   
model       1.40712   1.39334      1.40225      1.40361      1.37808   
use               0   1.39394       1.4048      1.40496      1.38116   
detect      1.39394         0      1.38898      1.39125      1.37088   
data         1.4048   1.38898  1.49012e-08      1.40332      1.37516   
dataset     1.40496   1.39125      1.40332  1.49012e-08      1.37577   
learn       1.38116   1.37088      1.37516      1.37577  1.49012e-08   
volum       1.26361   1.23337      1.26428      1.25897      1.23547   
infect      1.40043   1.38077      1.39526      1.39568       1.3702   

              volum    infect  
2020        1.14072   1.35672  
covid       1.26091    1.3992  
psycholog  0.780132  0.983519  
pandem      1.16492   1.34927  
health      1.18771   1.33008  
social     0.927891     1.079  
impact     0.830664  0.990138  
polici     0.785515  0.984038  
student     1.10202   1.26395  
countri    0.811453   1.00841  
imag        1.26349   1.39841  
model       1.26444   1.39689  
use         1.26361   1.40043  
detect      1.23337   1.38077  
data        1.26428   1.39526  
dataset     1.25897   1.39568  
learn       1.23547    1.3702  
volum             0   1.24947  
infect      1.24947         0  
Pairwise-distance
                  2020        covid psycholog       pandem    health  \
2020       2.98023e-08      1.40877   1.40522      1.40738    1.3938   
covid          1.40877  2.10734e-08   1.40445      1.40681   1.39476   
psycholog      1.40522      1.40445         0      1.40392   1.39104   
pandem         1.40738      1.40681   1.40392  2.58096e-08   1.39265   
health          1.3938      1.39476   1.39104      1.39265         0   
social         1.39547      1.39584    1.3899      1.39366   1.37967   
impact         1.39263      1.38994   1.38681      1.38987   1.37979   
polici          1.1199       1.1154   1.11575      1.11343   1.08604   
student        1.40566      1.40555   1.40058      1.40444   1.39078   
countri        1.37254       1.3736    1.3695      1.37533   1.35562   
imag           0.99628     0.997069  0.991081     0.995442  0.975322   
model           1.1505      1.15627   1.14737      1.14876   1.12853   
use            1.39346      1.39292   1.38969      1.39032   1.37669   
detect         0.99675     0.995804  0.990124     0.996323  0.975233   
data           1.36889      1.36728   1.36744      1.36669   1.35406   
dataset        0.99654     0.995543  0.990915     0.994854  0.974356   
learn           1.3435      1.34119   1.33791      1.34318   1.32206   
volum          1.04624       1.0557   1.03122      1.05393   1.02727   
infect         1.02955      1.01411   1.02566      1.03611   1.01404   

                social       impact    polici      student      countri  \
2020           1.39547      1.39263    1.1199      1.40566      1.37254   
covid          1.39584      1.38994    1.1154      1.40555       1.3736   
psycholog       1.3899      1.38681   1.11575      1.40058       1.3695   
pandem         1.39366      1.38987   1.11343      1.40444      1.37533   
health         1.37967      1.37979   1.08604      1.39078      1.35562   
social     1.49012e-08      1.37497   1.10581      1.39165      1.35905   
impact         1.37497  1.49012e-08   1.10301      1.38842      1.35693   
polici         1.10581      1.10301         0      1.11871      1.06742   
student        1.39165      1.38842   1.11871  1.49012e-08      1.37229   
countri        1.35905      1.35693   1.06742      1.37229  2.10734e-08   
imag          0.972575     0.973247  0.513694     0.993049     0.946728   
model          1.13741      1.13593  0.810979      1.15381      1.10904   
use            1.38183      1.37391   1.10091      1.38932       1.3529   
detect        0.978786     0.974056  0.516825     0.993995     0.938405   
data           1.35601        1.351   1.06916       1.3646      1.33185   
dataset       0.976866     0.972294  0.511738      0.99262     0.945482   
learn          1.33018      1.32593   1.04871      1.34294      1.30324   
volum          1.03505      1.03052  0.617643      1.05034      1.00522   
infect         1.01632      1.00619  0.593895      1.03266     0.971781   

                imag     model       use     detect         data    dataset  \
2020         0.99628    1.1505   1.39346    0.99675      1.36889    0.99654   
covid       0.997069   1.15627   1.39292   0.995804      1.36728   0.995543   
psycholog   0.991081   1.14737   1.38969   0.990124      1.36744   0.990915   
pandem      0.995442   1.14876   1.39032   0.996323      1.36669   0.994854   
health      0.975322   1.12853   1.37669   0.975233      1.35406   0.974356   
social      0.972575   1.13741   1.38183   0.978786      1.35601   0.976866   
impact      0.973247   1.13593   1.37391   0.974056        1.351   0.972294   
polici      0.513694  0.810979   1.10091   0.516825      1.06916   0.511738   
student     0.993049   1.15381   1.38932   0.993995       1.3646    0.99262   
countri     0.946728   1.10904    1.3529   0.938405      1.33185   0.945482   
imag               0   0.58304  0.975096  0.0511581     0.934351  0.0353732   
model        0.58304         0   1.11509   0.584022      1.09232   0.580425   
use         0.975096   1.11509         0   0.972546      1.35322   0.971641   
detect     0.0511581  0.584022  0.972546          0     0.931125   0.042182   
data        0.934351   1.09232   1.35322   0.931125  2.58096e-08   0.935488   
dataset    0.0353732  0.580425  0.971641   0.042182     0.935488          0   
learn       0.900102   1.07092   1.32179   0.903779       1.3097   0.902136   
volum       0.349022  0.689316   1.02235   0.351075      1.00189   0.347963   
infect      0.256458  0.623745   1.01873   0.253675     0.992864   0.259163   

                 learn        volum    infect  
2020            1.3435      1.04624   1.02955  
covid          1.34119       1.0557   1.01411  
psycholog      1.33791      1.03122   1.02566  
pandem         1.34318      1.05393   1.03611  
health         1.32206      1.02727   1.01404  
social         1.33018      1.03505   1.01632  
impact         1.32593      1.03052   1.00619  
polici         1.04871     0.617643  0.593895  
student        1.34294      1.05034   1.03266  
countri        1.30324      1.00522  0.971781  
imag          0.900102     0.349022  0.256458  
model          1.07092     0.689316  0.623745  
use            1.32179      1.02235   1.01873  
detect        0.903779     0.351075  0.253675  
data            1.3097      1.00189  0.992864  
dataset       0.902136     0.347963  0.259163  
learn      2.10734e-08     0.968708  0.945696  
volum         0.968708  7.45058e-09  0.429013  
infect        0.945696     0.429013         0  

(-0.25663333170429653, 0.8857, -1.1763298155997657)

## Vertical mantel test
### Considering two spaces in matel tests as
### Article level -----> 2-3 Paragraph level , 
### Article---->Paragraph level,
### Paragraph----> 2-3 Paragraph level

def mantel_test_verticle(model_a,model_b,n):
   
    #all words from the cleaned documents

    words_science = dict(model_a.show_topic(0, topn=word_count_science))
    words_psy = dict(model_b.show_topic(0, topn=word_count_psy))
   
    #top 5 words from the documents

    top_words_science = dict(model_a.show_topic(0, topn=n)) 
    top_words_psy = dict(model_b.show_topic(0,topn=n)) 

    #Finding out the weights of top 5 law words in  model_a level and updating their weights w.r.t model b dataset 
    c={} 
    for key in top_words_science:
        try:
            c[key] = words_psy[key]
        except KeyError:
            c[key] = 0

    top_words_psy.update(c)
    
    #Finding out the weights of top 5 law words in model b and updating their weights w.r.t model a dataset
    b={} 
    for key in top_words_psy:
        try:
            b[key] = words_science[key]
        except KeyError:
            b[key] = 0

    b.update(top_words_science)
    top_words_science=b

    #pairwise distance for top 10 words in model a
    a=pd.DataFrame()

    for i in range(number_of_topics):
        words_from_science =dict(model_a.show_topic(i, topn=word_count_science))
        b=pd.DataFrame(words_from_science,index=[i])
        a=a.append(b)

    a=a.transpose()

    df = pd.DataFrame(columns=list(top_words_science.keys()), 
                      index = list(top_words_science.keys()))

    l = list(top_words_science.keys())

    print('Pairwise-distance')
    for i in l:
        for j in l:
            try:
                matrix=np.array([a.loc[i],a.loc[j]])
            except KeyError:
                matrix=np.zeros((2,3))
            n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
            df.loc[[i],[j]] = n[0,1]
    print(df)
    df1 = df.to_numpy()
    #pairwise distance for top 10 words in model b
    a=pd.DataFrame()

    for i in range(number_of_topics):
        words_from_psy =dict(model_b.show_topic(i, topn=word_count_psy))
        b=pd.DataFrame(words_from_psy,index=[i])
        a=a.append(b)

    a=a.transpose()

    df = pd.DataFrame(columns=list(top_words_psy.keys()), 
                      index = list(top_words_psy.keys()))

    l = list(top_words_psy.keys())
    print('Pairwise-distance')
    for i in l:
        for j in l:
            try:
                matrix=np.array([a.loc[i],a.loc[j]])
            except KeyError:
                matrix=np.zeros((2,3))
            n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
            df.loc[[i],[j]] = n[0,1]
    print(df)
    df2 = df.to_numpy()
    

    dist1 = list(df1[np.triu_indices(len(top_words_science), k = 1)])

    

    dist2 = list(df2[np.triu_indices(len(top_words_psy), k = 1)])

    return mantel.test(dist1, dist2, perms=10000, method='pearson', tail='upper')

mantel_test_verticle(model_science_articles,model_science_p,5)

Pairwise-distance
           support         2020    granada    covid   programm     imag  \
support          0     0.694329   0.243552  1.42891   0.259057  1.65856   
2020      0.694329  1.05367e-08   0.589628  1.40568   0.581793   1.7231   
granada   0.243552     0.589628          0  1.42116  0.0533633  1.61975   
covid      1.42891      1.40568    1.42116        0    1.42763  2.03803   
programm  0.259057     0.581793  0.0533633  1.42763          0  1.61449   
imag       1.65856       1.7231    1.61975  2.03803    1.61449        0   
model      1.65136      1.57216    1.56798  2.07345    1.56797  2.33074   
use       0.884093      1.08919    0.92001  1.48408   0.920214  1.76843   
data       1.45815      1.71358    1.53404  1.90591    1.52988   2.2808   

            model       use     data  
support   1.65136  0.884093  1.45815  
2020      1.57216   1.08919  1.71358  
granada   1.56798   0.92001  1.53404  
covid     2.07345   1.48408  1.90591  
programm  1.56797  0.920214  1.52988  
imag      2.33074   1.76843   2.2808  
model           0   1.74983  2.18411  
use       1.74983         0  1.76952  
data      2.18411   1.76952        0  
Pairwise-distance
           support     2020   granada    covid  programm     imag    model  \
support          0  2.31274   1.14543  4.29945  0.893249  3.43628  2.85871   
2020       2.31274        0   1.24107   1.9984    1.7428  1.33987  1.47712   
granada    1.14543  1.24107         0  3.23626  0.524939  2.29085   1.7865   
covid      4.29945   1.9984   3.23626        0   3.72322  1.52372  2.54797   
programm  0.893249   1.7428  0.524939  3.72322         0  2.66513  1.98601   
imag       3.43628  1.33987   2.29085  1.52372   2.66513        0  1.04988   
model      2.85871  1.47712    1.7865  2.54797   1.98601  1.04988        0   
use        2.85871  1.47712    1.7865  2.54797   1.98601  1.04988        0   
data        1.7865  1.52372  0.913734   3.2917  0.893249   1.9984  1.14543   

              use      data  
support   2.85871    1.7865  
2020      1.47712   1.52372  
granada    1.7865  0.913734  
covid     2.54797    3.2917  
programm  1.98601  0.893249  
imag      1.04988    1.9984  
model           0   1.14543  
use             0   1.14543  
data      1.14543         0  

(0.1407286765878476, 0.2938, 0.5905114322083113)

mantel_test_verticle(model_science_articles,model_science_paragraph,5)

Pairwise-distance
          covid     imag    model      use   detect     data
covid         0  2.03803  2.07345  1.48408  1.46402  1.90591
imag    2.03803        0  2.33074  1.76843  1.66915   2.2808
model   2.07345  2.33074        0  1.74983  1.56905  2.18411
use     1.48408  1.76843  1.74983        0  1.14176  1.76952
detect  1.46402  1.66915  1.56905  1.14176        0  1.63152
data    1.90591   2.2808  2.18411  1.76952  1.63152        0
Pairwise-distance
              covid         imag    model      use       detect         data
covid   2.98023e-08      1.40622  1.40634  1.40901      1.39056      1.40437
imag        1.40622  1.49012e-08  1.40413  1.40753      1.39251      1.40654
model       1.40634      1.40413        0  1.40682      1.39364      1.40186
use         1.40901      1.40753  1.40682        0      1.39454       1.4047
detect      1.39056      1.39251  1.39364  1.39454  2.10734e-08      1.38899
data        1.40437      1.40654  1.40186   1.4047      1.38899  1.49012e-08

(0.5267820518396905, 0.19722222222222222, 1.207399320553872)

mantel_test_verticle(model_science_p,model_science_paragraph,5)

Pairwise-distance
            covid     imag    model      use   detect   support     2020  \
covid           0  1.52372  2.54797  2.54797  2.54797   4.29945   1.9984   
imag      1.52372        0  1.04988  1.04988  1.04988   3.43628  1.33987   
model     2.54797  1.04988        0        0        0   2.85871  1.47712   
use       2.54797  1.04988        0        0        0   2.85871  1.47712   
detect    2.54797  1.04988        0        0        0   2.85871  1.47712   
support   4.29945  3.43628  2.85871  2.85871  2.85871         0  2.31274   
2020       1.9984  1.33987  1.47712  1.47712  1.47712   2.31274        0   
granada   3.23626  2.29085   1.7865   1.7865   1.7865   1.14543  1.24107   
programm  3.72322  2.66513  1.98601  1.98601  1.98601  0.893249   1.7428   

           granada  programm  
covid      3.23626   3.72322  
imag       2.29085   2.66513  
model       1.7865   1.98601  
use         1.7865   1.98601  
detect      1.7865   1.98601  
support    1.14543  0.893249  
2020       1.24107    1.7428  
granada          0  0.524939  
programm  0.524939         0  
Pairwise-distance
                covid         imag     model       use       detect  \
covid     2.98023e-08      1.40622   1.40634   1.40901      1.39056   
imag          1.40622  1.49012e-08   1.40413   1.40753      1.39251   
model         1.40634      1.40413         0   1.40682      1.39364   
use           1.40901      1.40753   1.40682         0      1.39454   
detect        1.39056      1.39251   1.39364   1.39454  2.10734e-08   
support       1.05262      1.05762   1.05521   1.06195      1.05069   
2020          1.36761      1.36362     1.366   1.36655      1.35044   
granada      0.995973     0.993783  0.993354  0.996478     0.975627   
programm     0.995901     0.993696  0.993141  0.996464     0.975712   

              support      2020     granada     programm  
covid         1.05262   1.36761    0.995973     0.995901  
imag          1.05762   1.36362    0.993783     0.993696  
model         1.05521     1.366    0.993354     0.993141  
use           1.06195   1.36655    0.996478     0.996464  
detect        1.05069   1.35044    0.975627     0.975712  
support   1.05367e-08  0.996385    0.357782     0.352292  
2020         0.996385         0     0.93533     0.935082  
granada      0.357782   0.93533           0   0.00930787  
programm     0.352292  0.935082  0.00930787  1.64636e-10  

(-0.025229164267055907, 0.4952, -0.11650723344023295)

mantel_test_verticle(model_psy_articles,model_psy_p,5)

Pairwise-distance
             misinfodem        covid    articl    mechan   health  \
misinfodem  7.45058e-09      1.19883  0.650347  0.363944  1.34779   
covid           1.19883  2.98023e-08   1.17254   1.13414  1.67307   
articl         0.650347      1.17254         0  0.441214  1.32131   
mechan         0.363944      1.13414  0.441214         0   1.3049   
health          1.34779      1.67307   1.32131    1.3049        0   
2020            1.44097      1.48398   1.23638   1.37506  1.58891   
psycholog       1.21365      1.44499   1.05027    1.0956  1.58757   
student         1.59112      1.83153   1.54613   1.52132  2.09448   
pandem         0.913109      1.18534  0.766561  0.775584   1.2713   

                   2020 psycholog      student    pandem  
misinfodem      1.44097   1.21365      1.59112  0.913109  
covid           1.48398   1.44499      1.83153   1.18534  
articl          1.23638   1.05027      1.54613  0.766561  
mechan          1.37506    1.0956      1.52132  0.775584  
health          1.58891   1.58757      2.09448    1.2713  
2020        2.10734e-08   1.61227      2.10215   1.35571  
psycholog       1.61227         0      1.79664   1.24541  
student         2.10215   1.79664  2.98023e-08   1.65464  
pandem          1.35571   1.24541      1.65464         0  
Pairwise-distance
           misinfodem    covid    articl       mechan    health      2020  \
misinfodem          0  1.33926   2.07845      2.96288   2.96288   3.31627   
covid         1.33926        0   1.10881      1.65813   1.65813   2.07845   
articl        2.07845  1.10881         0      1.20521   1.20521   1.33926   
mechan        2.96288  1.65813   1.20521  1.86265e-08         0  0.554403   
health        2.96288  1.65813   1.20521            0         0  0.554403   
2020          3.31627  2.07845   1.33926     0.554403  0.554403         0   
psycholog     2.55053  1.66321  0.554403      1.31385   1.31385   1.20521   
student             0        0         0            0         0         0   
pandem        4.01777  2.67852   2.41043      1.20521   1.20521   1.31385   

              psycholog student       pandem  
misinfodem      2.55053       0      4.01777  
covid           1.66321       0      2.67852  
articl         0.554403       0      2.41043  
mechan          1.31385       0      1.20521  
health          1.31385       0      1.20521  
2020            1.20521       0      1.31385  
psycholog   2.63418e-08       0      2.45969  
student               0       0            0  
pandem          2.45969       0  2.63418e-08  

(-0.5439167986699588, 0.9719, -1.845298403327228)

mantel_test_verticle(model_psy_articles,model_psy_paragraph,5)

Pairwise-distance
                  2020        covid psycholog   pandem   health      student
2020       2.10734e-08      1.48398   1.61227  1.35571  1.58891      2.10215
covid          1.48398  2.98023e-08   1.44499  1.18534  1.67307      1.83153
psycholog      1.61227      1.44499         0  1.24541  1.58757      1.79664
pandem         1.35571      1.18534   1.24541        0   1.2713      1.65464
health         1.58891      1.67307   1.58757   1.2713        0      2.09448
student        2.10215      1.83153   1.79664  1.65464  2.09448  2.98023e-08
Pairwise-distance
                  2020    covid psycholog       pandem   health  student
2020       1.49012e-08  1.40884   1.40575      1.40701  1.39426  1.40523
covid          1.40884        0   1.40488      1.40624  1.39589  1.40523
psycholog      1.40575  1.40488         0      1.40369  1.39092  1.40062
pandem         1.40701  1.40624   1.40369  2.58096e-08  1.39311    1.404
health         1.39426  1.39589   1.39092      1.39311        0  1.38975
student        1.40523  1.40523   1.40062        1.404  1.38975        0

(-0.2379830910039947, 0.7527777777777778, -0.5501466047116179)

mantel_test_verticle(model_psy_p,model_psy_paragraph,5)

Pairwise-distance
                2020    covid    psycholog       pandem    health misinfodem  \
2020               0  2.07845      1.20521      1.31385  0.554403    3.31627   
covid        2.07845        0      1.66321      2.67852   1.65813    1.33926   
psycholog    1.20521  1.66321  2.63418e-08      2.45969   1.31385    2.55053   
pandem       1.31385  2.67852      2.45969  2.63418e-08   1.20521    4.01777   
health      0.554403  1.65813      1.31385      1.20521         0    2.96288   
misinfodem   3.31627  1.33926      2.55053      4.01777   2.96288          0   
articl       1.33926  1.10881     0.554403      2.41043   1.20521    2.07845   
mechan      0.554403  1.65813      1.31385      1.20521         0    2.96288   

              articl       mechan  
2020         1.33926     0.554403  
covid        1.10881      1.65813  
psycholog   0.554403      1.31385  
pandem       2.41043      1.20521  
health       1.20521            0  
misinfodem   2.07845      2.96288  
articl             0      1.20521  
mechan       1.20521  1.86265e-08  
Pairwise-distance
                   2020     covid psycholog       pandem    health  \
2020        1.49012e-08   1.40884   1.40575      1.40701   1.39426   
covid           1.40884         0   1.40488      1.40624   1.39589   
psycholog       1.40575   1.40488         0      1.40369   1.39092   
pandem          1.40701   1.40624   1.40369  2.58096e-08   1.39311   
health          1.39426   1.39589   1.39092      1.39311         0   
misinfodem     0.997802  0.993066    0.9921     0.993055  0.981802   
articl          1.25939   1.25332   1.26221      1.24738   1.24882   
mechan         0.996594  0.995498  0.990382     0.994853  0.976229   

             misinfodem    articl     mechan  
2020           0.997802   1.25939   0.996594  
covid          0.993066   1.25332   0.995498  
psycholog        0.9921   1.26221   0.990382  
pandem         0.993055   1.24738   0.994853  
health         0.981802   1.24882   0.976229  
misinfodem  1.31709e-09  0.754778  0.0444107  
articl         0.754778         0   0.762522  
mechan        0.0444107  0.762522          0  

(-0.269143448460167, 0.7513, -0.8977474365216084)

mantel_test_verticle(model_science_articles,model_science_p,10)

Pairwise-distance
           support         2020    granada    covid   programm  research  \
support          0     0.694329   0.243552  1.42891   0.259057  0.456066   
2020      0.694329  1.05367e-08   0.589628  1.40568   0.581793  0.597341   
granada   0.243552     0.589628          0  1.42116  0.0533633  0.353174   
covid      1.42891      1.40568    1.42116        0    1.42763   1.38175   
programm  0.259057     0.581793  0.0533633  1.42763          0  0.346901   
research  0.456066     0.597341   0.353174  1.38175   0.346901         0   
tabik     0.239679     0.599147  0.0405284  1.41732  0.0936909  0.363266   
garcía    0.251375     0.586015  0.0314383  1.42391  0.0273959   0.35068   
project   0.341003     0.617683   0.202709  1.44224    0.17841  0.351158   
decsai    0.254221     0.583304  0.0400225  1.42583  0.0133408  0.347713   
imag       1.65856       1.7231    1.61975  2.03803    1.61449   1.61237   
model      1.65136      1.57216    1.56798  2.07345    1.56797   1.61128   
use       0.884093      1.08919    0.92001  1.48408   0.920214   0.98799   
data       1.45815      1.71358    1.53404  1.90591    1.52988   1.52479   
dataset    1.21082      1.35324    1.15603  1.90698    1.16299   1.16379   
infect     1.32072      1.32195    1.28007  1.83833    1.26905   1.33793   
base      0.625979      1.02554   0.713701  1.55093   0.713798  0.810955   
test       1.28183       1.3043    1.22959  1.76269    1.22238   1.28598   
perform   0.781917     0.968413   0.778485  1.57285   0.763601  0.875571   

                tabik     garcía   project     decsai     imag    model  \
support      0.239679   0.251375  0.341003   0.254221  1.65856  1.65136   
2020         0.599147   0.586015  0.617683   0.583304   1.7231  1.57216   
granada     0.0405284  0.0314383  0.202709  0.0400225  1.61975  1.56798   
covid         1.41732    1.42391   1.44224    1.42583  2.03803  2.07345   
programm    0.0936909  0.0273959   0.17841  0.0133408  1.61449  1.56797   
research     0.363266    0.35068  0.351158   0.347713  1.61237  1.61128   
tabik     1.86265e-09  0.0704339  0.227368  0.0803753   1.6249  1.56976   
garcía      0.0704339          0  0.179201   0.016615  1.61636  1.56833   
project      0.227368   0.179201         0   0.183334  1.59436   1.5964   
decsai      0.0803753   0.016615  0.183334          0  1.61564   1.5678   
imag           1.6249    1.61636   1.59436    1.61564        0  2.33074   
model         1.56976    1.56833    1.5964     1.5678  2.33074        0   
use          0.921543   0.917462  0.895918   0.919872  1.76843  1.74983   
data          1.53861    1.53198   1.55572    1.53075   2.2808  2.18411   
dataset       1.15249    1.16089   1.14508    1.16102  1.79918  1.94443   
infect        1.28595    1.27455    1.2617     1.2716  2.08225  1.89989   
base          0.71632   0.711333  0.716432     0.7134  1.77436  1.71891   
test          1.23616     1.2244   1.22845    1.22397    2.117  1.99214   
perform      0.792043   0.771749  0.802654   0.767001  1.74953  1.53652   

               use     data  dataset   infect      base     test   perform  
support   0.884093  1.45815  1.21082  1.32072  0.625979  1.28183  0.781917  
2020       1.08919  1.71358  1.35324  1.32195   1.02554   1.3043  0.968413  
granada    0.92001  1.53404  1.15603  1.28007  0.713701  1.22959  0.778485  
covid      1.48408  1.90591  1.90698  1.83833   1.55093  1.76269   1.57285  
programm  0.920214  1.52988  1.16299  1.26905  0.713798  1.22238  0.763601  
research   0.98799  1.52479  1.16379  1.33793  0.810955  1.28598  0.875571  
tabik     0.921543  1.53861  1.15249  1.28595   0.71632  1.23616  0.792043  
garcía    0.917462  1.53198  1.16089  1.27455  0.711333   1.2244  0.771749  
project   0.895918  1.55572  1.14508   1.2617  0.716432  1.22845  0.802654  
decsai    0.919872  1.53075  1.16102   1.2716    0.7134  1.22397  0.767001  
imag       1.76843   2.2808  1.79918  2.08225   1.77436    2.117   1.74953  
model      1.74983  2.18411  1.94443  1.89989   1.71891  1.99214   1.53652  
use              0  1.76952  1.34406  1.63891   1.05984  1.38242  0.939696  
data       1.76952        0   1.8182  2.05822   1.57974  1.99553   1.65029  
dataset    1.34406   1.8182        0   1.8766   1.44021   1.6322   1.41743  
infect     1.63891  2.05822   1.8766        0   1.35622  1.73726   1.57978  
base       1.05984  1.57974  1.44021  1.35622         0  1.41242  0.952681  
test       1.38242  1.99553   1.6322  1.73726   1.41242        0   1.43564  
perform   0.939696  1.65029  1.41743  1.57978  0.952681  1.43564         0  
Pairwise-distance
           support      2020   granada    covid  programm  research     tabik  \
support          0   2.31274   1.14543  4.29945  0.893249  0.893249   1.55841   
2020       2.31274         0   1.24107   1.9984    1.7428    1.7428   1.04988   
granada    1.14543   1.24107         0  3.23626  0.524939  0.524939  0.446625   
covid      4.29945    1.9984   3.23626        0   3.72322   3.72322   2.97988   
programm  0.893249    1.7428  0.524939  3.72322         0         0  0.770914   
research  0.893249    1.7428  0.524939  3.72322         0         0  0.770914   
tabik      1.55841   1.04988  0.446625  2.97988  0.770914  0.770914         0   
garcía     2.29085  0.596458   1.14543  2.25512   1.54183   1.54183  0.770914   
project    1.33987   1.57482  0.596458   3.4856  0.446625  0.446625  0.524939   
decsai     1.33987   1.57482  0.596458   3.4856  0.446625  0.446625  0.524939   
imag       3.43628   1.33987   2.29085  1.52372   2.66513   2.66513   1.90083   
model      2.85871   1.47712    1.7865  2.54797   1.98601   1.98601   1.33987   
use        2.85871   1.47712    1.7865  2.54797   1.98601   1.98601   1.33987   
data        1.7865   1.52372  0.913734   3.2917  0.893249  0.893249  0.596458   
dataset    2.85871   1.47712    1.7865  2.54797   1.98601   1.98601   1.33987   
infect           0         0         0        0         0         0         0   
base       2.85871   1.47712    1.7865  2.54797   1.98601   1.98601   1.33987   
test       3.11682   1.30882   1.98601  2.03258   2.29085   2.29085   1.55841   
perform          0         0         0        0         0         0         0   

            garcía   project    decsai      imag        model          use  \
support    2.29085   1.33987   1.33987   3.43628      2.85871      2.85871   
2020      0.596458   1.57482   1.57482   1.33987      1.47712      1.47712   
granada    1.14543  0.596458  0.596458   2.29085       1.7865       1.7865   
covid      2.25512    3.4856    3.4856   1.52372      2.54797      2.54797   
programm   1.54183  0.446625  0.446625   2.66513      1.98601      1.98601   
research   1.54183  0.446625  0.446625   2.66513      1.98601      1.98601   
tabik     0.770914  0.524939  0.524939   1.90083      1.33987      1.33987   
garcía           0   1.24107   1.24107   1.14543     0.913734     0.913734   
project    1.24107         0         0   2.31274      1.55841      1.55841   
decsai     1.24107         0         0   2.31274      1.55841      1.55841   
imag       1.14543   2.31274   2.31274         0      1.04988      1.04988   
model     0.913734   1.55841   1.55841   1.04988            0            0   
use       0.913734   1.55841   1.55841   1.04988            0            0   
data       1.04988  0.446625  0.446625    1.9984      1.14543      1.14543   
dataset   0.913734   1.55841   1.55841   1.04988            0            0   
infect           0         0         0         0            0            0   
base      0.913734   1.55841   1.55841   1.04988  6.58545e-09  6.58545e-09   
test      0.893249   1.90083   1.90083  0.524939     0.524939     0.524939   
perform          0         0         0         0            0            0   

              data      dataset infect         base      test perform  
support     1.7865      2.85871      0      2.85871   3.11682       0  
2020       1.52372      1.47712      0      1.47712   1.30882       0  
granada   0.913734       1.7865      0       1.7865   1.98601       0  
covid       3.2917      2.54797      0      2.54797   2.03258       0  
programm  0.893249      1.98601      0      1.98601   2.29085       0  
research  0.893249      1.98601      0      1.98601   2.29085       0  
tabik     0.596458      1.33987      0      1.33987   1.55841       0  
garcía     1.04988     0.913734      0     0.913734  0.893249       0  
project   0.446625      1.55841      0      1.55841   1.90083       0  
decsai    0.446625      1.55841      0      1.55841   1.90083       0  
imag        1.9984      1.04988      0      1.04988  0.524939       0  
model      1.14543            0      0  6.58545e-09  0.524939       0  
use        1.14543            0      0  6.58545e-09  0.524939       0  
data             0      1.14543      0      1.14543   1.54183       0  
dataset    1.14543            0      0  6.58545e-09  0.524939       0  
infect           0            0      0            0         0       0  
base       1.14543  6.58545e-09      0  9.31323e-09  0.524939       0  
test       1.54183     0.524939      0     0.524939         0       0  
perform          0            0      0            0         0       0  

(0.10997676111624198, 0.2862, 0.6143788521576996)

mantel_test_verticle(model_science_articles,model_science_paragraph,10)

Pairwise-distance
           covid     imag    model       use    detect     data  dataset  \
covid          0  2.03803  2.07345   1.48408   1.46402  1.90591  1.90698   
imag     2.03803        0  2.33074   1.76843   1.66915   2.2808  1.79918   
model    2.07345  2.33074        0   1.74983   1.56905  2.18411  1.94443   
use      1.48408  1.76843  1.74983         0   1.14176  1.76952  1.34406   
detect   1.46402  1.66915  1.56905   1.14176         0  1.63152  1.32155   
data     1.90591   2.2808  2.18411   1.76952   1.63152        0   1.8182   
dataset  1.90698  1.79918  1.94443   1.34406   1.32155   1.8182        0   
learn    1.76094  1.97963    1.949   1.33702   1.24658  1.94162  1.71229   
volum    1.39123  1.60994  1.58588  0.921589  0.676524  1.54176  1.16116   
infect   1.83833  2.08225  1.89989   1.63891   1.43678  2.05822   1.8766   
base     1.55093  1.77436  1.71891   1.05984  0.873666  1.57974  1.44021   
test     1.76269    2.117  1.99214   1.38242   1.31697  1.99553   1.6322   
perform  1.57285  1.74953  1.53652  0.939696  0.880592  1.65029  1.41743   

           learn     volum   infect      base     test   perform  
covid    1.76094   1.39123  1.83833   1.55093  1.76269   1.57285  
imag     1.97963   1.60994  2.08225   1.77436    2.117   1.74953  
model      1.949   1.58588  1.89989   1.71891  1.99214   1.53652  
use      1.33702  0.921589  1.63891   1.05984  1.38242  0.939696  
detect   1.24658  0.676524  1.43678  0.873666  1.31697  0.880592  
data     1.94162   1.54176  2.05822   1.57974  1.99553   1.65029  
dataset  1.71229   1.16116   1.8766   1.44021   1.6322   1.41743  
learn          0   1.14628   1.5898   1.24358  1.71254   1.24927  
volum    1.14628         0  1.15268  0.752205  1.26437  0.783574  
infect    1.5898   1.15268        0   1.35622  1.73726   1.57978  
base     1.24358  0.752205  1.35622         0  1.41242  0.952681  
test     1.71254   1.26437  1.73726   1.41242        0   1.43564  
perform  1.24927  0.783574  1.57978  0.952681  1.43564         0  
Pairwise-distance
               covid         imag    model      use       detect         data  \
covid    2.98023e-08      1.40622  1.40634  1.40901      1.39056      1.40437   
imag         1.40622  1.49012e-08  1.40413  1.40753      1.39251      1.40654   
model        1.40634      1.40413        0  1.40682      1.39364      1.40186   
use          1.40901      1.40753  1.40682        0      1.39454       1.4047   
detect       1.39056      1.39251  1.39364  1.39454  2.10734e-08      1.38899   
data         1.40437      1.40654  1.40186   1.4047      1.38899  1.49012e-08   
dataset      1.40387      1.40547   1.4034  1.40501      1.39202      1.40339   
learn        1.38011      1.37824   1.3785  1.38105      1.36961      1.37519   
volum         1.2496      1.25104  1.25463  1.25256      1.22118       1.2524   
infect       1.39786      1.39674  1.39547  1.39863      1.38205      1.39314   
base         1.40057      1.39988  1.39832  1.40219      1.38677       1.3966   
test         1.40569      1.40376   1.4037  1.40504      1.39086      1.40057   
perform       1.3988      1.39738  1.39939  1.39944      1.38594      1.39423   

             dataset    learn    volum   infect         base     test  \
covid        1.40387  1.38011   1.2496  1.39786      1.40057  1.40569   
imag         1.40547  1.37824  1.25104  1.39674      1.39988  1.40376   
model         1.4034   1.3785  1.25463  1.39547      1.39832   1.4037   
use          1.40501  1.38105  1.25256  1.39863      1.40219  1.40504   
detect       1.39202  1.36961  1.22118  1.38205      1.38677  1.39086   
data         1.40339  1.37519   1.2524  1.39314       1.3966  1.40057   
dataset  1.49012e-08  1.37642  1.24946  1.39417      1.39686  1.40174   
learn        1.37642        0  1.22286  1.37121      1.37407  1.37745   
volum        1.24946  1.22286        0   1.2303      1.25767  1.25148   
infect       1.39417  1.37121   1.2303        0      1.38852  1.39527   
base         1.39686  1.37407  1.25767  1.38852  1.49012e-08  1.39746   
test         1.40174  1.37745  1.25148  1.39527      1.39746        0   
perform      1.39488  1.36819  1.24312  1.38699      1.39177  1.39318   

             perform  
covid         1.3988  
imag         1.39738  
model        1.39939  
use          1.39944  
detect       1.38594  
data         1.39423  
dataset      1.39488  
learn        1.36819  
volum        1.24312  
infect       1.38699  
base         1.39177  
test         1.39318  
perform  2.98023e-08  

(0.5077909941233221, 0.0087, 1.7862279888090011)

mantel_test_verticle(model_science_p,model_science_paragraph,10)

Pairwise-distance
            covid     imag     model       use    detect      data   dataset  \
covid           0  1.52372   2.54797   2.54797   2.54797    3.2917   2.54797   
imag      1.52372        0   1.04988   1.04988   1.04988    1.9984   1.04988   
model     2.54797  1.04988         0         0         0   1.14543         0   
use       2.54797  1.04988         0         0         0   1.14543         0   
detect    2.54797  1.04988         0         0         0   1.14543         0   
data       3.2917   1.9984   1.14543   1.14543   1.14543         0   1.14543   
dataset   2.54797  1.04988         0         0         0   1.14543         0   
learn     2.54797  1.04988         0         0         0   1.14543         0   
volum           0        0         0         0         0         0         0   
infect          0        0         0         0         0         0         0   
support   4.29945  3.43628   2.85871   2.85871   2.85871    1.7865   2.85871   
2020       1.9984  1.33987   1.47712   1.47712   1.47712   1.52372   1.47712   
granada   3.23626  2.29085    1.7865    1.7865    1.7865  0.913734    1.7865   
programm  3.72322  2.66513   1.98601   1.98601   1.98601  0.893249   1.98601   
research  3.72322  2.66513   1.98601   1.98601   1.98601  0.893249   1.98601   
tabik     2.97988  1.90083   1.33987   1.33987   1.33987  0.596458   1.33987   
garcía    2.25512  1.14543  0.913734  0.913734  0.913734   1.04988  0.913734   
project    3.4856  2.31274   1.55841   1.55841   1.55841  0.446625   1.55841   
decsai     3.4856  2.31274   1.55841   1.55841   1.55841  0.446625   1.55841   

             learn volum infect   support      2020   granada  programm  \
covid      2.54797     0      0   4.29945    1.9984   3.23626   3.72322   
imag       1.04988     0      0   3.43628   1.33987   2.29085   2.66513   
model            0     0      0   2.85871   1.47712    1.7865   1.98601   
use              0     0      0   2.85871   1.47712    1.7865   1.98601   
detect           0     0      0   2.85871   1.47712    1.7865   1.98601   
data       1.14543     0      0    1.7865   1.52372  0.913734  0.893249   
dataset          0     0      0   2.85871   1.47712    1.7865   1.98601   
learn            0     0      0   2.85871   1.47712    1.7865   1.98601   
volum            0     0      0         0         0         0         0   
infect           0     0      0         0         0         0         0   
support    2.85871     0      0         0   2.31274   1.14543  0.893249   
2020       1.47712     0      0   2.31274         0   1.24107    1.7428   
granada     1.7865     0      0   1.14543   1.24107         0  0.524939   
programm   1.98601     0      0  0.893249    1.7428  0.524939         0   
research   1.98601     0      0  0.893249    1.7428  0.524939         0   
tabik      1.33987     0      0   1.55841   1.04988  0.446625  0.770914   
garcía    0.913734     0      0   2.29085  0.596458   1.14543   1.54183   
project    1.55841     0      0   1.33987   1.57482  0.596458  0.446625   
decsai     1.55841     0      0   1.33987   1.57482  0.596458  0.446625   

          research     tabik    garcía   project    decsai  
covid      3.72322   2.97988   2.25512    3.4856    3.4856  
imag       2.66513   1.90083   1.14543   2.31274   2.31274  
model      1.98601   1.33987  0.913734   1.55841   1.55841  
use        1.98601   1.33987  0.913734   1.55841   1.55841  
detect     1.98601   1.33987  0.913734   1.55841   1.55841  
data      0.893249  0.596458   1.04988  0.446625  0.446625  
dataset    1.98601   1.33987  0.913734   1.55841   1.55841  
learn      1.98601   1.33987  0.913734   1.55841   1.55841  
volum            0         0         0         0         0  
infect           0         0         0         0         0  
support   0.893249   1.55841   2.29085   1.33987   1.33987  
2020        1.7428   1.04988  0.596458   1.57482   1.57482  
granada   0.524939  0.446625   1.14543  0.596458  0.596458  
programm         0  0.770914   1.54183  0.446625  0.446625  
research         0  0.770914   1.54183  0.446625  0.446625  
tabik     0.770914         0  0.770914  0.524939  0.524939  
garcía     1.54183  0.770914         0   1.24107   1.24107  
project   0.446625  0.524939   1.24107         0         0  
decsai    0.446625  0.524939   1.24107         0         0  
Pairwise-distance
                covid         imag     model       use       detect  \
covid     2.98023e-08      1.40622   1.40634   1.40901      1.39056   
imag          1.40622  1.49012e-08   1.40413   1.40753      1.39251   
model         1.40634      1.40413         0   1.40682      1.39364   
use           1.40901      1.40753   1.40682         0      1.39454   
detect        1.39056      1.39251   1.39364   1.39454  2.10734e-08   
data          1.40437      1.40654   1.40186    1.4047      1.38899   
dataset       1.40387      1.40547    1.4034   1.40501      1.39202   
learn         1.38011      1.37824    1.3785   1.38105      1.36961   
volum          1.2496      1.25104   1.25463   1.25256      1.22118   
infect        1.39786      1.39674   1.39547   1.39863      1.38205   
support       1.05262      1.05762   1.05521   1.06195      1.05069   
2020          1.36761      1.36362     1.366   1.36655      1.35044   
granada      0.995973     0.993783  0.993354  0.996478     0.975627   
programm     0.995901     0.993696  0.993141  0.996464     0.975712   
research      1.11813      1.11662   1.11224   1.11797      1.08658   
tabik        0.993075     0.992203  0.994312  0.997236     0.978552   
garcía       0.995882     0.993605  0.993181  0.996324     0.975646   
project      0.999343     0.995844  0.995479  0.995363     0.980782   
decsai       0.995889     0.993602  0.993181  0.996372     0.975657   

                 data      dataset     learn     volum    infect      support  \
covid         1.40437      1.40387   1.38011    1.2496   1.39786      1.05262   
imag          1.40654      1.40547   1.37824   1.25104   1.39674      1.05762   
model         1.40186       1.4034    1.3785   1.25463   1.39547      1.05521   
use            1.4047      1.40501   1.38105   1.25256   1.39863      1.06195   
detect        1.38899      1.39202   1.36961   1.22118   1.38205      1.05069   
data      1.49012e-08      1.40339   1.37519    1.2524   1.39314      1.05282   
dataset       1.40339  1.49012e-08   1.37642   1.24946   1.39417      1.05348   
learn         1.37519      1.37642         0   1.22286   1.37121       1.0236   
volum          1.2524      1.24946   1.22286         0    1.2303     0.810471   
infect        1.39314      1.39417   1.37121    1.2303         0      1.04017   
support       1.05282      1.05348    1.0236  0.810471   1.04017  1.05367e-08   
2020          1.36153      1.36046   1.33648   1.12978   1.35655     0.996385   
granada      0.989451     0.991206  0.954511   0.76303  0.981382     0.357782   
programm     0.989893     0.991128  0.954515  0.763545  0.981042     0.352292   
research      1.10917      1.11368    1.0798  0.950689   1.10433      0.48387   
tabik        0.990364     0.987062  0.955192  0.766769  0.981884     0.360837   
garcía       0.989871     0.991123  0.954593  0.763377  0.981231     0.357986   
project      0.991491     0.993723  0.957798  0.769111  0.983816     0.349722   
decsai       0.989859     0.991142  0.954559  0.763103  0.981197     0.358727   

              2020     granada     programm  research        tabik  \
covid      1.36761    0.995973     0.995901   1.11813     0.993075   
imag       1.36362    0.993783     0.993696   1.11662     0.992203   
model        1.366    0.993354     0.993141   1.11224     0.994312   
use        1.36655    0.996478     0.996464   1.11797     0.997236   
detect     1.35044    0.975627     0.975712   1.08658     0.978552   
data       1.36153    0.989451     0.989893   1.10917     0.990364   
dataset    1.36046    0.991206     0.991128   1.11368     0.987062   
learn      1.33648    0.954511     0.954515    1.0798     0.955192   
volum      1.12978     0.76303     0.763545  0.950689     0.766769   
infect     1.35655    0.981382     0.981042   1.10433     0.981884   
support   0.996385    0.357782     0.352292   0.48387     0.360837   
2020             0     0.93533     0.935082   1.04922     0.936481   
granada    0.93533           0   0.00930787  0.500955    0.0210427   
programm  0.935082  0.00930787  1.64636e-10  0.496895    0.0211718   
research   1.04922    0.500955     0.496895         0     0.500109   
tabik     0.936481   0.0210427    0.0211718  0.500109  5.70316e-10   
garcía    0.935359   0.0065583   0.00807549   0.50083    0.0202775   
project   0.933113    0.077307    0.0763875  0.487427     0.080191   
decsai    0.935385  0.00582306   0.00799752  0.502257    0.0198681   

               garcía    project      decsai  
covid        0.995882   0.999343    0.995889  
imag         0.993605   0.995844    0.993602  
model        0.993181   0.995479    0.993181  
use          0.996324   0.995363    0.996372  
detect       0.975646   0.980782    0.975657  
data         0.989871   0.991491    0.989859  
dataset      0.991123   0.993723    0.991142  
learn        0.954593   0.957798    0.954559  
volum        0.763377   0.769111    0.763103  
infect       0.981231   0.983816    0.981197  
support      0.357986   0.349722    0.358727  
2020         0.935359   0.933113    0.935385  
granada     0.0065583   0.077307  0.00582306  
programm   0.00807549  0.0763875  0.00799752  
research      0.50083   0.487427    0.502257  
tabik       0.0202775   0.080191   0.0198681  
garcía    8.23181e-11  0.0777376  0.00410933  
project     0.0777376          0   0.0781554  
decsai     0.00410933  0.0781554           0  

(0.05924934236844332, 0.3857, 0.3309664504130001)

mantel_test_verticle(model_psy_articles,model_psy_p,10)

Pairwise-distance
             misinfodem        covid    articl    mechan   health    commun  \
misinfodem  7.45058e-09      1.19883  0.650347  0.363944  1.34779  0.967211   
covid           1.19883  2.98023e-08   1.17254   1.13414  1.67307   1.54277   
articl         0.650347      1.17254         0  0.441214  1.32131  0.945772   
mechan         0.363944      1.13414  0.441214         0   1.3049  0.921909   
health          1.34779      1.67307   1.32131    1.3049        0   1.64385   
commun         0.967211      1.54277  0.945772  0.921909  1.64385         0   
strategi       0.419676      1.19176  0.490353  0.327267  1.33014  0.944397   
psycholog       1.21365      1.44499   1.05027    1.0956  1.58757    1.2675   
misinform      0.179496      1.15976  0.525198  0.193913  1.31222  0.932335   
public          0.42451      1.16644  0.803435  0.535868  1.26582   1.05285   
2020            1.44097      1.48398   1.23638   1.37506  1.58891   1.64904   
student         1.59112      1.83153   1.54613   1.52132  2.09448   1.79046   
pandem         0.913109      1.18534  0.766561  0.775584   1.2713   1.24686   
train           1.09312      1.51068   1.13737   1.01737  1.66229   1.32348   
social          1.17094      1.59148   1.31419   1.11875  1.74427   1.35459   
time           0.653929      1.23044  0.583662  0.513851  1.38668   1.03213   
group           1.63806      1.85046   1.51706   1.54104  2.06298   1.88821   

               strategi psycholog misinform    public         2020  \
misinfodem     0.419676   1.21365  0.179496   0.42451      1.44097   
covid           1.19176   1.44499   1.15976   1.16644      1.48398   
articl         0.490353   1.05027  0.525198  0.803435      1.23638   
mechan         0.327267    1.0956  0.193913  0.535868      1.37506   
health          1.33014   1.58757   1.31222   1.26582      1.58891   
commun         0.944397    1.2675  0.932335   1.05285      1.64904   
strategi    7.45058e-09   1.10447  0.337644   0.59557      1.37627   
psycholog       1.10447         0   1.13955   1.09417      1.61227   
misinform      0.337644   1.13955         0  0.451701      1.39108   
public          0.59557   1.09417  0.451701         0      1.53995   
2020            1.37627   1.61227   1.39108   1.53995  2.10734e-08   
student         1.50576   1.79664   1.54924   1.66659      2.10215   
pandem         0.824393   1.24541   0.83498  0.931131      1.35571   
train           1.11281   1.34992   1.03579   1.10731      1.74142   
social          1.20951   1.66799   1.13695   1.21017      1.68498   
time           0.564604   1.12283  0.551543  0.755122      1.28826   
group           1.58366    1.8687   1.58001     1.717      2.00183   

                student    pandem    train       social      time    group  
misinfodem      1.59112  0.913109  1.09312      1.17094  0.653929  1.63806  
covid           1.83153   1.18534  1.51068      1.59148   1.23044  1.85046  
articl          1.54613  0.766561  1.13737      1.31419  0.583662  1.51706  
mechan          1.52132  0.775584  1.01737      1.11875  0.513851  1.54104  
health          2.09448    1.2713  1.66229      1.74427   1.38668  2.06298  
commun          1.79046   1.24686  1.32348      1.35459   1.03213  1.88821  
strategi        1.50576  0.824393  1.11281      1.20951  0.564604  1.58366  
psycholog       1.79664   1.24541  1.34992      1.66799   1.12283   1.8687  
misinform       1.54924   0.83498  1.03579      1.13695  0.551543  1.58001  
public          1.66659  0.931131  1.10731      1.21017  0.755122    1.717  
2020            2.10215   1.35571  1.74142      1.68498   1.28826  2.00183  
student     2.98023e-08   1.65464   1.7198      1.88077   1.62523  2.21137  
pandem          1.65464         0  1.09245       1.3521  0.941849  1.76379  
train            1.7198   1.09245        0      1.59658   1.06769  1.81971  
social          1.88077    1.3521  1.59658  2.10734e-08   1.20581  1.74298  
time            1.62523  0.941849  1.06769      1.20581         0  1.57572  
group           2.21137   1.76379  1.81971      1.74298   1.57572        0  
Pairwise-distance
           misinfodem    covid    articl       mechan    health       commun  \
misinfodem          0  1.33926   2.07845      2.96288   2.96288      2.96288   
covid         1.33926        0   1.10881      1.65813   1.65813      1.65813   
articl        2.07845  1.10881         0      1.20521   1.20521      1.20521   
mechan        2.96288  1.65813   1.20521  1.86265e-08         0  1.86265e-08   
health        2.96288  1.65813   1.20521            0         0            0   
commun        2.96288  1.65813   1.20521  1.86265e-08         0            0   
strategi      2.55053  1.66321  0.554403      1.31385   1.31385      1.31385   
psycholog     2.55053  1.66321  0.554403      1.31385   1.31385      1.31385   
misinform     3.57135   2.2321   2.01252     0.819898  0.819898     0.819898   
public        2.89364  1.70035  0.892838     0.538987  0.538987     0.538987   
2020          3.31627  2.07845   1.33926     0.554403  0.554403     0.554403   
student             0        0         0            0         0            0   
pandem        4.01777  2.67852   2.41043      1.20521   1.20521      1.20521   
train               0        0         0            0         0            0   
social        4.01777  2.67852   2.41043      1.20521   1.20521      1.20521   
time          3.74442  2.47937   1.78568     0.850176  0.850176     0.850176   
group               0        0         0            0         0            0   

            strategi    psycholog misinform    public      2020 student  \
misinfodem   2.55053      2.55053   3.57135   2.89364   3.31627       0   
covid        1.66321      1.66321    2.2321   1.70035   2.07845       0   
articl      0.554403     0.554403   2.01252  0.892838   1.33926       0   
mechan       1.31385      1.31385  0.819898  0.538987  0.554403       0   
health       1.31385      1.31385  0.819898  0.538987  0.554403       0   
commun       1.31385      1.31385  0.819898  0.538987  0.554403       0   
strategi           0            0   2.12281  0.819898   1.20521       0   
psycholog          0  2.63418e-08   2.12281  0.819898   1.20521       0   
misinform    2.12281      2.12281         0   1.31385   1.07797       0   
public      0.819898     0.819898   1.31385         0  0.446419       0   
2020         1.20521      1.20521   1.07797  0.446419         0       0   
student            0            0         0         0         0       0   
pandem       2.45969      2.45969  0.446419    1.6398   1.31385       0   
train              0            0         0         0         0       0   
social       2.45969      2.45969  0.446419    1.6398   1.31385       0   
time         1.62217      1.62217  0.998224  0.892838  0.446419       0   
group              0            0         0         0         0       0   

                 pandem train       social      time group  
misinfodem      4.01777     0      4.01777   3.74442     0  
covid           2.67852     0      2.67852   2.47937     0  
articl          2.41043     0      2.41043   1.78568     0  
mechan          1.20521     0      1.20521  0.850176     0  
health          1.20521     0      1.20521  0.850176     0  
commun          1.20521     0      1.20521  0.850176     0  
strategi        2.45969     0      2.45969   1.62217     0  
psycholog       2.45969     0      2.45969   1.62217     0  
misinform      0.446419     0     0.446419  0.998224     0  
public           1.6398     0       1.6398  0.892838     0  
2020            1.31385     0      1.31385  0.446419     0  
student               0     0            0         0     0  
pandem      2.63418e-08     0  2.63418e-08   1.07797     0  
train                 0     0            0         0     0  
social      2.63418e-08     0  2.63418e-08   1.07797     0  
time            1.07797     0      1.07797         0     0  
group                 0     0            0         0     0  

(-0.46962449360728215, 0.9914, -2.3102322654980028)

mantel_test_verticle(model_psy_articles,model_psy_paragraph,10)

Pairwise-distance
                  2020        covid psycholog    pandem   health       social  \
2020       2.10734e-08      1.48398   1.61227   1.35571  1.58891      1.68498   
covid          1.48398  2.98023e-08   1.44499   1.18534  1.67307      1.59148   
psycholog      1.61227      1.44499         0   1.24541  1.58757      1.66799   
pandem         1.35571      1.18534   1.24541         0   1.2713       1.3521   
health         1.58891      1.67307   1.58757    1.2713        0      1.74427   
social         1.68498      1.59148   1.66799    1.3521  1.74427  2.10734e-08   
impact         1.30498      1.18023   1.12893  0.795946  1.50378      1.34668   
polici         1.28835      1.14473   1.07787  0.788964  1.39658      1.23092   
student        2.10215      1.83153   1.79664   1.65464  2.09448      1.88077   
countri        1.63319     0.863671   1.24316   1.07916  1.60392       1.4604   
train          1.74142      1.51068   1.34992   1.09245  1.66229      1.59658   
time           1.28826      1.23044   1.12283  0.941849  1.38668      1.20581   
group          2.00183      1.85046    1.8687   1.76379  2.06298      1.74298   

             impact    polici      student   countri    train      time  \
2020        1.30498   1.28835      2.10215   1.63319  1.74142   1.28826   
covid       1.18023   1.14473      1.83153  0.863671  1.51068   1.23044   
psycholog   1.12893   1.07787      1.79664   1.24316  1.34992   1.12283   
pandem     0.795946  0.788964      1.65464   1.07916  1.09245  0.941849   
health      1.50378   1.39658      2.09448   1.60392  1.66229   1.38668   
social      1.34668   1.23092      1.88077    1.4604  1.59658   1.20581   
impact            0  0.604577      1.71028   1.00235  1.15796  0.901602   
polici     0.604577         0        1.605  0.857284  1.04642  0.536625   
student     1.71028     1.605  2.98023e-08   1.80054   1.7198   1.62523   
countri     1.00235  0.857284      1.80054         0  1.33181   1.02877   
train       1.15796   1.04642       1.7198   1.33181        0   1.06769   
time       0.901602  0.536625      1.62523   1.02877  1.06769         0   
group       1.71713   1.58251      2.21137   1.80538  1.81971   1.57572   

             group  
2020       2.00183  
covid      1.85046  
psycholog   1.8687  
pandem     1.76379  
health     2.06298  
social     1.74298  
impact     1.71713  
polici     1.58251  
student    2.21137  
countri    1.80538  
train      1.81971  
time       1.57572  
group            0  
Pairwise-distance
                  2020    covid psycholog       pandem   health       social  \
2020       1.49012e-08  1.40884   1.40575      1.40701  1.39426      1.39534   
covid          1.40884        0   1.40488      1.40624  1.39589      1.39547   
psycholog      1.40575  1.40488         0      1.40369  1.39092      1.39006   
pandem         1.40701  1.40624   1.40369  2.58096e-08  1.39311      1.39342   
health         1.39426  1.39589   1.39092      1.39311        0      1.38081   
social         1.39534  1.39547   1.39006      1.39342  1.38081  1.49012e-08   
impact         1.39096  1.38816   1.38423      1.38872  1.37945      1.37293   
polici          1.1165  1.11175   1.11126      1.10648  1.08446      1.10119   
student        1.40523  1.40523   1.40062        1.404  1.38975      1.39187   
countri          1.366  1.36543   1.35909       1.3701  1.34507      1.34844   
train          1.38867  1.38821   1.38527       1.3882  1.37585      1.37528   
time           1.39868  1.39559   1.39452       1.3941  1.38254      1.38499   
group          1.40032  1.39832   1.39717      1.39751  1.38187      1.38688   

            impact   polici  student  countri    train         time  \
2020       1.39096   1.1165  1.40523    1.366  1.38867      1.39868   
covid      1.38816  1.11175  1.40523  1.36543  1.38821      1.39559   
psycholog  1.38423  1.11126  1.40062  1.35909  1.38527      1.39452   
pandem     1.38872  1.10648    1.404   1.3701   1.3882       1.3941   
health     1.37945  1.08446  1.38975  1.34507  1.37585      1.38254   
social     1.37293  1.10119  1.39187  1.34844  1.37528      1.38499   
impact           0  1.09708  1.38681   1.3552  1.36908      1.37667   
polici     1.09708        0  1.11538  1.03559  1.09004      1.10311   
student    1.38681  1.11538        0  1.36704  1.38338      1.39334   
countri     1.3552  1.03559  1.36704        0  1.34199       1.3518   
train      1.36908  1.09004  1.38338  1.34199        0      1.37578   
time       1.37667  1.10311  1.39334   1.3518  1.37578  2.10734e-08   
group      1.38089  1.10855  1.39611  1.35498  1.37768      1.38856   

                 group  
2020           1.40032  
covid          1.39832  
psycholog      1.39717  
pandem         1.39751  
health         1.38187  
social         1.38688  
impact         1.38089  
polici         1.10855  
student        1.39611  
countri        1.35498  
train          1.37768  
time           1.38856  
group      1.49012e-08  

(0.43606282874936597, 0.0159, 1.5557018850462125)

mantel_test_verticle(model_psy_p,model_psy_paragraph,10)

Pairwise-distance
                2020    covid    psycholog       pandem    health  \
2020               0  2.07845      1.20521      1.31385  0.554403   
covid        2.07845        0      1.66321      2.67852   1.65813   
psycholog    1.20521  1.66321  2.63418e-08      2.45969   1.31385   
pandem       1.31385  2.67852      2.45969  2.63418e-08   1.20521   
health      0.554403  1.65813      1.31385      1.20521         0   
social       1.31385  2.67852      2.45969  2.63418e-08   1.20521   
impact      0.538987  2.20461     0.892838      1.83335  0.998224   
polici             0        0            0            0         0   
student            0        0            0            0         0   
countri            0        0            0            0         0   
misinfodem   3.31627  1.33926      2.55053      4.01777   2.96288   
articl       1.33926  1.10881     0.554403      2.41043   1.20521   
mechan      0.554403  1.65813      1.31385      1.20521         0   
commun      0.554403  1.65813      1.31385      1.20521         0   
strategi     1.20521  1.66321            0      2.45969   1.31385   
misinform    1.07797   2.2321      2.12281     0.446419  0.819898   
public      0.446419  1.70035     0.819898       1.6398  0.538987   

                 social    impact polici student countri misinfodem    articl  \
2020            1.31385  0.538987      0       0       0    3.31627   1.33926   
covid           2.67852   2.20461      0       0       0    1.33926   1.10881   
psycholog       2.45969  0.892838      0       0       0    2.55053  0.554403   
pandem      2.63418e-08   1.83335      0       0       0    4.01777   2.41043   
health          1.20521  0.998224      0       0       0    2.96288   1.20521   
social      2.63418e-08   1.83335      0       0       0    4.01777   2.41043   
impact          1.83335         0      0       0       0    3.31522   1.23968   
polici                0         0      0       0       0          0         0   
student               0         0      0       0       0          0         0   
countri               0         0      0       0       0          0         0   
misinfodem      4.01777   3.31522      0       0       0          0   2.07845   
articl          2.41043   1.23968      0       0       0    2.07845         0   
mechan          1.20521  0.998224      0       0       0    2.96288   1.20521   
commun          1.20521  0.998224      0       0       0    2.96288   1.20521   
strategi        2.45969  0.892838      0       0       0    2.55053  0.554403   
misinform      0.446419   1.61696      0       0       0    3.57135   2.01252   
public           1.6398  0.554403      0       0       0    2.89364  0.892838   

                 mechan       commun  strategi misinform    public  
2020           0.554403     0.554403   1.20521   1.07797  0.446419  
covid           1.65813      1.65813   1.66321    2.2321   1.70035  
psycholog       1.31385      1.31385         0   2.12281  0.819898  
pandem          1.20521      1.20521   2.45969  0.446419    1.6398  
health                0            0   1.31385  0.819898  0.538987  
social          1.20521      1.20521   2.45969  0.446419    1.6398  
impact         0.998224     0.998224  0.892838   1.61696  0.554403  
polici                0            0         0         0         0  
student               0            0         0         0         0  
countri               0            0         0         0         0  
misinfodem      2.96288      2.96288   2.55053   3.57135   2.89364  
articl          1.20521      1.20521  0.554403   2.01252  0.892838  
mechan      1.86265e-08  1.86265e-08   1.31385  0.819898  0.538987  
commun      1.86265e-08            0   1.31385  0.819898  0.538987  
strategi        1.31385      1.31385         0   2.12281  0.819898  
misinform      0.819898     0.819898   2.12281         0   1.31385  
public         0.538987     0.538987  0.819898   1.31385         0  
Pairwise-distance
                   2020     covid psycholog       pandem    health  \
2020        1.49012e-08   1.40884   1.40575      1.40701   1.39426   
covid           1.40884         0   1.40488      1.40624   1.39589   
psycholog       1.40575   1.40488         0      1.40369   1.39092   
pandem          1.40701   1.40624   1.40369  2.58096e-08   1.39311   
health          1.39426   1.39589   1.39092      1.39311         0   
social          1.39534   1.39547   1.39006      1.39342   1.38081   
impact          1.39096   1.38816   1.38423      1.38872   1.37945   
polici           1.1165   1.11175   1.11126      1.10648   1.08446   
student         1.40523   1.40523   1.40062        1.404   1.38975   
countri           1.366   1.36543   1.35909       1.3701   1.34507   
misinfodem     0.997802  0.993066    0.9921     0.993055  0.981802   
articl          1.25939   1.25332   1.26221      1.24738   1.24882   
mechan         0.996594  0.995498  0.990382     0.994853  0.976229   
commun          1.39987   1.39936   1.39679      1.39762   1.38177   
strategi        1.00755    1.0104   0.99986      1.00199  0.989408   
misinform      0.994462   0.99556  0.992649     0.994971  0.973708   
public          1.38831   1.38869   1.38689      1.38576   1.36089   

                 social    impact    polici   student   countri   misinfodem  \
2020            1.39534   1.39096    1.1165   1.40523     1.366     0.997802   
covid           1.39547   1.38816   1.11175   1.40523   1.36543     0.993066   
psycholog       1.39006   1.38423   1.11126   1.40062   1.35909       0.9921   
pandem          1.39342   1.38872   1.10648     1.404    1.3701     0.993055   
health          1.38081   1.37945   1.08446   1.38975   1.34507     0.981802   
social      1.49012e-08   1.37293   1.10119   1.39187   1.34844     0.976833   
impact          1.37293         0   1.09708   1.38681    1.3552      0.96703   
polici          1.10119   1.09708         0   1.11538   1.03559     0.505232   
student         1.39187   1.38681   1.11538         0   1.36704     0.993527   
countri         1.34844    1.3552   1.03559   1.36704         0     0.937604   
misinfodem     0.976833   0.96703  0.505232  0.993527  0.937604  1.31709e-09   
articl          1.24103   1.21861  0.904385   1.25176   1.20212     0.754778   
mechan         0.974832  0.970893  0.502508  0.992238  0.935711    0.0444107   
commun          1.38727   1.37702   1.11454   1.39674   1.35454     0.985897   
strategi       0.986059  0.997831  0.535365   1.00027  0.919945     0.147045   
misinform      0.976298   0.97254  0.503874  0.992809  0.935308    0.0395313   
public          1.37731    1.3655   1.08156   1.38651   1.34416     0.955795   

              articl     mechan       commun  strategi    misinform  \
2020         1.25939   0.996594      1.39987   1.00755     0.994462   
covid        1.25332   0.995498      1.39936    1.0104      0.99556   
psycholog    1.26221   0.990382      1.39679   0.99986     0.992649   
pandem       1.24738   0.994853      1.39762   1.00199     0.994971   
health       1.24882   0.976229      1.38177  0.989408     0.973708   
social       1.24103   0.974832      1.38727  0.986059     0.976298   
impact       1.21861   0.970893      1.37702  0.997831      0.97254   
polici      0.904385   0.502508      1.11454  0.535365     0.503874   
student      1.25176   0.992238      1.39674   1.00027     0.992809   
countri      1.20212   0.935711      1.35454  0.919945     0.935308   
misinfodem  0.754778  0.0444107     0.985897  0.147045    0.0395313   
articl             0   0.762522      1.25005  0.778417      0.76117   
mechan      0.762522          0     0.984178  0.147471    0.0375953   
commun       1.25005   0.984178  2.58096e-08  0.985311     0.979919   
strategi    0.778417   0.147471     0.985311         0     0.145483   
misinform    0.76117  0.0375953     0.979919  0.145483  9.31323e-10   
public       1.24555   0.964688      1.38324  0.962206     0.956896   

                 public  
2020            1.38831  
covid           1.38869  
psycholog       1.38689  
pandem          1.38576  
health          1.36089  
social          1.37731  
impact           1.3655  
polici          1.08156  
student         1.38651  
countri         1.34416  
misinfodem     0.955795  
articl          1.24555  
mechan         0.964688  
commun          1.38324  
strategi       0.962206  
misinform      0.956896  
public      1.49012e-08  

(-0.19957243325925628, 0.8382, -0.990172106766751)